hypothesis based on the impact of tropical cyclone

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Article contents

The global climatology of tropical cyclones.

• Hamish Ramsay Hamish Ramsay School of Earth, Atmosphere, and Environment, Monash University; ARC Centre of Excellence for Climate System Science
• https://doi.org/10.1093/acrefore/9780199389407.013.79
• Published online: 24 May 2017

Tropical cyclones, also known as hurricanes or typhoons, are one of the most violent weather phenomena on the planet, posing significant threats to those living near or along coastlines where tropical cyclone–related impacts are most pronounced. About 80 tropical cyclones form annually, a rate that has been remarkably steady over the period of reliable historical record. Roughly two thirds of these storms form in the Northern Hemisphere from about June to November, while the remaining third form in the Southern Hemisphere typically during the months of November to May. Our understanding of the global and regional spatial patterns, the year-to-year variability, and temporal trends of these storms has improved considerably since the advent of meteorological satellites in the 1960s because of advances in both remote-sensing technology and operational analysis procedures. The well-recognized spatial patterns of tropical cyclone formation and tracks were laid out in a series of seminal papers in the late 1960s and 1970s and remain an accurate sketch even to this day. Concerning the year-to-year variability of tropical cyclone frequency, the El Niño Southern Oscillation (ENSO) has by far the most dominant influence across multiple ocean basins, so much so that it is typically used as the main predictor for statistical forecasts of seasonal tropical cyclone activity. ENSO has a modulating influence on atmospheric circulation patterns, even in regions remote to the tropical Pacific, which, in turn, can act to enhance or inhibit tropical cyclone formation.

While the meteorological and climate community has come a long way in our understanding of the global and regional climatological features of tropical cyclones, as well as some aspects of the broader relationship between tropical cyclones and climate, we are still hindered by temporal inconsistencies within the historical record of storm data, particularly pertaining to tropical cyclone intensity. Despite recent efforts to homogenize the historical record using satellite-derived intensity data back to the early 1980s, the relatively short period makes it difficult to discern secular trends due to anthropogenic climate change from natural trends occurring on decadal to multidecadal time scales.

• tropical cyclone
• climatology
• El Niño-Southern Oscillation (ENSO)
• variability
• interannual

Introduction

Tropical cyclones (TCs) are among the most powerful and destructive weather systems on earth. At peak intensity, surface winds around the inner core have been observed to exceed 90 m s -1 (333 km h -1 ; 207 mph) (Kimberlain, Blake, & Cangialosi, 2016 ). In addition to their violent surface winds, tropical cyclones bring with them additional hazards of torrential rain and powerful storm surges, which individually may pose even greater threats than the wind, especially to those living along the coast (e.g., Rappaport, 2000 , 2013 ; Rappaport & Blanchard, 2015 ).

There is no one concise and agreed-upon definition of the term “tropical cyclone,” arguably because it has different meanings depending on geographic location. In the meteorological community, a tropical cyclone is the general term for a “cyclone that originates over the tropical oceans” (American Meteorological Society, 2016 ), or, more technically, a non-frontal synoptic scale low-pressure system over tropical and subtropical waters with organized convection (i.e., thunderstorm activity) and a closed cyclonic low-level wind circulation. Most people, however, know these systems by their regionally specific names. People living in the United States, for instance, refer to tropical cyclones as tropical depressions, tropical storms, or hurricanes, depending on their intensity, whereas people living in Asia use the term “typhoon” to describe a tropical cyclone that has reached hurricane intensity.

Tropical Cyclone Nomenclature and “Best Track” Data

Knowledge of the global and regional characteristics of tropical cyclone activity, including where and when they form, their intensities, and tracks, requires careful documentation and planned coordination from government agencies around the world. In 1972 , the World Meteorological Organization (WMO) established the Tropical Cyclone Project, a predecessor to the Tropical Cyclone Programme (TCP) currently in operation, in response to the disastrous 1970 Bangladesh cyclone. The TCP has established six regional specialized meteorological centers (RSMCs) and six tropical cyclone warning centers (TCWCs) (Fig. 1 ), which are responsible for providing real-time warnings to the public with information such as a tropical cyclone’s location, size, present and forecast movement, and intensity, as well as archiving best estimates of location and intensity into historical datasets known as “best track” data. The best track data are used widely in studies of the regional and global impacts of climate variability and change on tropical cyclones. The geographical subregions shown in Figure 1 are grouped typically into seven global tropical cyclone basins: (i) North Atlantic (NA, region 1), (ii) Eastern North Pacific (ENP, regions 2 and 3), (iii) Western North Pacific (WNP, region 4), (iv) North Indian (NI, region 5), (v) South Indian (SI, region 6), (vi) Australian (AUS, regions 7, 8, 9, 10, and 11), and (vii) the South Pacific (SP, regions 12 and 13). While it is possible for tropical cyclones to form in the South Atlantic, instances are exceptionally rare (a notable exception was Hurricane Catarina in March 2004 ; McTaggart-Cowan et al., 2006 ; Pezza & Simmonds, 2005 ), and consequently it is not recognized officially as a tropical cyclone basin. Finally, there are a number of other government agencies, such as the Joint Typhoon Warning Center (JTWC) in Hawaii, which do not fall under the organizational structure of the WMO but nevertheless provide valuable real-time advisories and warnings of tropical cyclones and contribute to the collection of best track data.

The International Best Track Archive for Climate Stewardship (IBTrACS; Knapp, Kruk, Levinson, Diamond, & Neumann, 2010 ) provides a merged global dataset of tropical cyclone frequency, location, and intensity, collected from all the international centers (both WMO and non-WMO affiliated) for public use. The IBTrACS-WMO best track data for the period 1985–2014 are used for the climatology presented herein, with a system being counted as a tropical cyclone if its maximum sustained surface wind (MSSW) speed reaches at least 34 knots (17.5 m s -1 ) (corresponding to gale-force winds on the Beaufort scale). Most of the WMO-sanctioned agencies use a 10-minute average MSSW speed, following WMO guidelines, but the U.S. National Hurricane Center (NHC) and the India Meteorological Department (IMD) use a 1-minute and 3-minute average, respectively, to calculate the MSSW speed. For the purpose of this review, the NHC and IMD data are converted to a 10-minute average by multiplying by a factor of 0.88—a commonly used although by no means optimal conversion factor whose origins remain somewhat unclear (Harper, Kepert, & Ginger, 2010 ). The interested reader is referred to Harper et al. ( 2010 ) for a detailed discussion on converting between various wind-averaging periods in tropical cyclone conditions.

Figure 1. Map showing the six Regional Specialized Meteorological Centers (RSMCs) and the six Tropical Cyclone Warning Centers (TCWCs) assigned by the World Meteorological Tropical Cyclone Programme.

Public confusion commonly arises from the many different naming conventions used to classify tropical cyclones depending on their intensity, not only between the global basins but also within a particular basin. In the North Atlantic and Eastern North Pacific basin, including the Gulf of Mexico and the Caribbean Sea, tropical cyclones are referred to as tropical depressions, tropical storms, or hurricanes, depending on intensity. Tropical depressions and tropical storms have maximum sustained surface winds of less than and greater than 17 m s -1 , respectively, whereas a hurricane is more intense, with maximum sustained surface winds of at least 33 m s -1 . In the Western North Pacific basin, tropical cyclones are classified as tropical depressions (<17 m s -1 ), tropical storms (>17 m s -1 ), severe tropical storms (>24 m s -1 ), and typhoons (≥33 m s -1 ). In the North Indian Ocean, the naming convention is different yet again, with the term cyclonic storm favored over tropical storm, and very severe cyclonic storm used instead of hurricane. In the Southwest Indian Ocean, terms such as moderate tropical storm (>17 m s -1 ), severe tropical storm (>24 m s -1 ), tropical cyclone (≥33 m s -1 ), intense tropical cyclone (>44 m s -1 ), and very intense tropical cyclone (>58 m s -1 ) are used. Finally, in the Australian and South Pacific regions, the term tropical cyclone itself is used to describe systems whose MSSW speeds exceed 17 m s -1 , whereas a severe tropical cyclone refers to a tropical cyclone with a MSSW speed of at least 33 m s -1 .

Global and Hemispheric Aspects of Tropical Cyclone Formation and Frequency

Each year, around 80 tropical cyclones form around the globe (Fig. 2 )—a number that has been remarkably steady since the start of reliable global best track data. The annual global rate of tropical cyclone formation continues to mystify climate scientists (Emanuel & Nolan, 2004 ); that there are about 80 tropical cyclones, and not 40 or 160, for instance, is a statistic without a satisfactory explanation, and there is no extant theory for what sets the global rate of formation (Walsh et al., 2015 ). During the 25-year period from 1990 to 2014 , when full global data are available in IBTrACS-WMO, an average annual number of 79 tropical cyclones occurred (based on a 10-minute average MSSW speed exceeding 17 m s -1 ), with a standard deviation of about seven. The global average number of storms varies slightly depending on dataset used and the wind-averaging period (e.g., a 1-minute versus a 10-minute average MSSW speed), but studies typically report somewhere between 80 and 90 tropical cyclones per year (e.g., Frank & Young, 2007 ; Lander & Guard, 1998 ; Schreck, Knapp, & Kossin, 2014 ). The most active years globally in the period 1990–2014 were 1992 , 2005 , and 2013 —when 90 tropical cyclones formed—while the most inactive years were 1999 (65 TCs) and 2010 (69 TCs; Maue, 2011 ), respectively (Fig. 2 ).

Figure 2. Time series of global, hemispheric, and regional annual tropical cyclones counts for the period 1985 to 2014. The North Indian basin has a shorter complete period of best track data, beginning in 1990. Note that a tropical cyclone year in the Southern Hemisphere is defined from July 1 to June 30 (e.g., 2004 spans from July 1, 2003, to June 30, 2004). See Schreck et al. ( 2014 ) for a comparison of annual storm counts from IBTrACS-WMO and NHC+JTWC over the period 1981–2010.

The Northern Hemisphere is host to a disproportionate number of tropical cyclones, experiencing 70% of the global total, compared to just 30% in the Southern Hemisphere. Almost one third (31%) of all storms originate and track over the warm waters of the Western North Pacific, with the Eastern North Pacific and North Atlantic basins housing 19% and 16%, respectively. The North Indian basin experiences significantly fewer tropical cyclones, accounting for only about 4% of the global total (Fig. 3 ), despite it being one of the most vulnerable regions of the world in terms of tropical cyclone impacts (Peduzzi et al., 2012 ).

Figure 3. (Top panel): Figure from Gray ( 1968 ) showing the “location points of first detection of disturbances which later became tropical storms.” ( © American Meteorological Society. Used with permission.) (Bottom panel): The global distribution of tropical cyclone formation points for the period of most reliable global best track data, 1985–2014. The percentage of tropical cyclones occurring in each basin (relative to the global total) is also shown based on data from 1990 to 2014.

In the Southern Hemisphere, tropical cyclones form in a semi-continuous latitudinal zone, stretching from the east coast of Africa to the central South Pacific Ocean. This long expanse of formation points in the Southern Hemisphere (Fig. 3 ) makes the dividing lines between individual tropical cyclone basins less obvious compared to the Northern Hemisphere, where land acts often as a natural barrier. Nevertheless, these basin boundary lines serve to separate the areas of responsibility assigned by the WMO to various government agencies, as shown in Figure 1 . Of the 30% of global tropical cyclones that develop in the Southern Hemisphere, on average, 11% occur in the South Indian basin, 12% in the Australian region, and 7% in the South Pacific basin (Fig. 3 ). The fraction of the global total of tropical cyclone activity that each basin represents is insensitive to choice of best track data, with IBTrACS-WMO and NHC+JTWC producing similar statistics (Schreck et al., 2014 ).

Seasonality, Variability and Trends in Tropical Cyclone Counts

There was no discernable trend in the global number of tropical cyclones from 1985 to 2014 or from 1990 to 2014 (Fig. 2 ), despite some studies predicting a decrease in response to increased greenhouse gases (Knutson et al., 2010 ). The most active months for tropical cyclones globally are August and September, which together account for about one third (31%) of the total annual number. The month of May sees the fewest tropical cyclones, with an average of just three per year, or 4% of the annual global total.

Tropical cyclones in the Northern Hemisphere can form during any month of the year, although most storms (90%) occur from June to November, with a peak in August–September (44%). The quietest period in the Northern Hemisphere is from January to March, which coincides with the most active part of the cyclone season in the Southern Hemisphere. The years 1992 and 2013 were the most active in the Northern Hemisphere for the period 1990–2014 , with 67 storms occurring in each of those years compared to the hemispheric average of 55, whereas only 44 storms formed in 1999 and 2010 . There was no significant trend in the annual number of tropical cyclones in the Northern Hemisphere from 1985 to 2014 or from 1990 to 2014 .

In the Southern Hemisphere, tropical cyclones form typically during the months of November to April (94% of the annual count), with the peak of activity occurring during January–March (66% of the annual count), although at least one storm has formed in every month except for August based on data from 1985 to 2014 . The off-season months, from June to October, coincide with the most active period in the Northern Hemisphere. The year 1997 (i.e., July 1996 to June 1997 ) was the most active in the period 1985–2014, with 33 tropical cyclones forming (9 above the long-term average of 24), whereas less than half that number, 16, formed in 1991 . There was a slight downward, albeit insignificant, trend in the total annual number of Southern Hemisphere tropical cyclones from 1985 to 2014 —a decrease of roughly one storm per decade.

North Atlantic Basin

The North Atlantic tropical cyclone season, known as “hurricane season ” in the United States, runs officially from June 1 to November 30, with 97% of storms occurring in that period. In rare instances storms have occurred outside the official season, such as Tropical Storm Ana in April 2003 , or Tropical Storm Alberto in May 2012 . The peak months in the North Atlantic are August and September (Fig. 4 ), which see an average of about seven tropical cyclones forming in the basin (i.e., 58% of the annual total count). In 2005 , a record-breaking number of storms formed in a single season, totaling 27 tropical cyclones (based on a 10-minute average MSSW speed exceeding 17 m s -1 ), which surpassed the previous record set during the post-satellite era by a staggering nine tropical cyclones. Fifteen of the 27 storms became hurricanes, including three Category 5 systems (Katrina, Rita, and Wilma), breaking the preceding record for the number of hurricanes in one season (Beven et al., 2008 ). In contrast, only six tropical cyclones formed in 1986 , about half the annual average of 12. There was a significant upward trend in the annual number of tropical cyclones in the North Atlantic from 1985 to 2014 , at a rate of about 2.4 storms per decade (Fig. 2 ), as has been noted in other studies for similar time periods (e.g., Kossin, Camargo, & Sitkowski, 2010 ; Kossin, Olander, & Knapp, 2013 ).

Figure 4. Histograms of monthly frequency of tropical cyclone formation count for the globe, each hemisphere, and seven individual basins, based on data from 1985 to 2014. Note that the Southern Hemisphere annual cycle begins in July and ends in June. See Schreck et al. ( 2014 ) for a similar analysis based on data from both IBTrACS-WMO and NHC+JTWC.

Eastern North Pacific Basin

The Eastern North Pacific hurricane season runs from May 15 to November 30 , although the majority of storms (93%) form between June and October. The seasonal cycle of tropical cyclone activity is shifted slightly compared to its North Atlantic neighbor, beginning in May and peaking in August (Fig. 4 ), and it is extremely rare for storms to develop outside the main months of May to November, with just four systems forming out of season between 1985 and 2014 . Notably, Tropical Storm Hali in March 1992 was the only known tropical cyclone to form east of the Date Line in the month of March, and earlier that year Hurricane Ekeka formed as a tropical cyclone in late January, becoming the first observed Hurricane in the Central Pacific during the month of January in the post-satellite era. The basin typically has around 15 tropical cyclones annually, but this number has ranged from as low as 8 in 1999 and 2010 to a record-breaking 26 in 1992 . The basin boasts the highest density of tropical cyclone activity of anywhere in the world (Figs. 3 , 5 , 6 ). There was no discernable trend in the annual number of tropical cyclones from 1985 to 2014 .

Western North Pacific Basin

The Western North Pacific (WNP) basin is rather unique in terms of its tropical cyclone climatology, being the only basin in the world to have had at least one storm form in every calendar month (Fig. 4 ); however, most storms (95%) form between May and December. The seasonal peak in activity occurs in August and September (45%), with an average of 10 tropical cyclones each year, making it the most active basin for formation anywhere in the world. The basin experiences about the same number annually as all the regions of the Southern Hemisphere combined (~24 tropical cyclones). Even during the off-season months from January to April, tropical cyclones have been known to occur. Typhoon Mitag, for instance, developed in late of February 2002 near the Federal States of Micronesia, subsequently intensifying to become the first Super Typhoon on record for the month of March. The most active year in the period 1985 –2014 was 1994 , when a total of 32 tropical cyclones formed, including 19 typhoons and 6 super typhoons (Lander & Guard, 1998 ). In contrast, just 14 tropical cyclones formed during 2010 , making it the least active tropical cyclone season on record for the Western North Pacific. There was a significant downward trend in the annual number of tropical cyclones from 1985 to 2014 , at a rate of about 2.4 storms per decade, which interestingly is of the same magnitude but opposite sign to the observed increase in the North Atlantic basin over the same period.

North Indian Basin

Unlike other basins that display unimodal seasonal cycles of tropical cyclone activity, the North Indian basin has two distinct peaks (Fig. 4 ; Evan & Camargo, 2011 ; Gray, 1968 ; Li, Yu, Li, Murty, & Tangang, 2013 ) associated with the seasonal migration of the South Asian monsoon. The first peak occurs in April to June, accounting for 35% of the average annual total, but most storms develop in the post-monsoon period from October to December (about 60% of the annual average count). The North Indian basin has the lowest frequency of storms relative to the annual global total (Fig. 3 ), with just 77 tropical cyclones forming in the 25-year period 1990–2014 , or roughly 3 per year. The region was the last of the WMO-sanctioned agencies to include routine documentation of wind speed data as part of their best tracks (relatively complete best track data date back to 1990 ), and thus it is difficult to gauge long-term trends there (Kossin et al., 2013 ; Landsea et al., 2006 ; Schreck et al., 2014 ). The most active years in the basin were 1996 , 1998 , and 2010 , all with five tropical cyclones (i.e., two above the long-term average), whereas only one storm formed in 2001 , 2002 , and 2011 . There was almost no trend in the annual number of tropical cyclones in the period 1990 to 2014 .

South Indian Ocean Basin

The South Indian Ocean basin is the westernmost basin the Southern Hemisphere, extending from the African coast to 90°E. It is home to 11% of the global annual storm count (Fig. 3 ) and 35% of the Southern Hemisphere count. Storms generally form between November and April (92%), with almost two thirds occurring in January–March (Fig. 4 ). Although less common, tropical cyclones occasionally develop in the months flanking the main season—October (about one every 5 years) and May (about one every 10 years). Since 1985 , the most active seasons were 1994 and 2003 , when 13 storms formed. The quietest season, 1999 , saw only three tropical cyclones develop. There has been a small positive, albeit insignificant, upward trend in the number of storms forming in the region from 1985 to 2014 (Fig. 2 ).

Australian Region

The Australian tropical cyclone region extends from 90°E to 160°E, south of the equator, and is part of a continuum of tropical cyclone activity that extends from the coast of Africa to French Polynesia in the South Pacific (Fig. 3 ). The official tropical cyclone season is from November 1 to April 30, with 94% of storms forming during this period, but at least one tropical cyclone has formed in every month except for August. It is also not uncommon to have cyclones form in May (there were nine in the 30-year period 1985–2014 ), despite the last day of the official tropical cyclone season being April 30. The most active months are from January to March, when an average of six storms form in the region. The annual average count in the most recent 30-year period is somewhat lower than if the entire post-satellite period (i.e., 1970 onwards) is considered, owing to some very active years in the 1970s and early 1980s. Nevertheless, for the period 1985–2014 the average annual count was about 9 storms per year, while the observed count for any given year has ranged from as many as 14 systems in 1985 , 1986 , and 1999 , to as few as just 5 in 1988 and 2007 . The most active seasons on record were 1974 and 1984 (i.e., 1973–74 and 1983–84 ), with 19 tropical cyclones in the Australian region (Blair Trewin, personal communication). There was a downward but statistically insignificant trend in the annual number of tropical cyclones between 1985 and 2014 .

South Pacific Basin

The South Pacific basin, which is situated directly to the east of the Australian region (Fig. 3 ), extends from 160°E to 120°W. Its western boundary at 160°E serves to separate the Australian Bureau of Meteorology’s region of responsibility from that of the Meteorological Services of Fiji and New Zealand (Fig. 1 ). The South Pacific basin has the lowest rate of tropical cyclone formation in the Southern Hemisphere, accounting for 7% of the average global annual rate (Figs. 2 , 3 ), or roughly six systems per year. The seasonality of storms is very similar to the Australian region, with 95% forming between November 1 and April 30. No tropical cyclones formed between the months of July to September during the period 1985 to 2014 . The basin has the largest year-to-year (interannual) variability of tropical cyclone frequency of any basin in the world, with a standard deviation of more than half the annual average rate ( μ ‎ = 6, σ ‎ = 3.5). The most active year was 1998 , with a record-breaking 15 tropical cyclones forming in the basin, whereas only one system formed in both 1991 and 2002 . A downward trend of about 0.8 storms per decade was present from 1985 to 2014 , though this trend was not statistically significant.

Interannual Variability of Tropical Cyclone Counts

The interannual variability of tropical cyclones, both globally and regionally, continues to be a major area of research in the tropical cyclone community. The annual global number of tropical cyclones has been relatively steady at about 80 or so ( μ ‎ = 78.3, σ ‎ = 6.9) since the start of reliable best track records. The rather small standard deviation of the annual global storm count, compared to the mean, has led to the commonly held view that the annual global count is more stable than might be expected given the large interannual variability present in individual basins; however, Frank and Young ( 2007 ) found contradictory evidence of this, demonstrating that the global variability of storm numbers is in fact indistinguishable from that when each basin was examined independently of the others. The interannual variability of tropical cyclone counts on the scale of individual basins is driven primarily by natural modes of variability such as the El Niño Southern Oscillation (ENSO), the Atlantic Meridional Mode (AMM), and the Quasi-Biennial Oscillation (QBO). Of these modes, ENSO has by far the most dominant influence on tropical cyclone variability across multiple basins.

The first study to establish the feasibility of seasonal tropical cyclone prediction was Nicholls ( 1979 ), “A Possible Method for Predicting Seasonal Tropical Cyclone Activity in the Australian Region.” This pioneering study revealed that the year-to-year fluctuations in the number of tropical cyclones in the Australian region were linked to pressure anomalies at Darwin during the preceding winters, which, in turn, were linked to the Southern Oscillation component of ENSO. The relationship was found to be negative, such that anomalously low pressure associated with La Niña events was a precursor to an active season, whereas anomalously high pressure (during El Niño events) was an indicator of below-average tropical cyclone activity. Nicholls ( 1984 ) confirmed his earlier results using a longer dataset in addition to two other large-scale predictors related to ENSO: (i) sea surface temperature (SST) in the eastern Equatorial Pacific and (ii) SST in the region immediately to the north of Australia where many tropical cyclones develop. The results further validated Nicholls’s earlier work, indicating that ENSO indices could be used to predict seasonal tropical cyclone activity in the Australian region months before the official November 1 onset. Since Nicholls’s founding work, many other studies have shown strong statistical links between ENSO and Australian tropical cyclone activity, confirming that El Niño (La Niña) years are associated with below (above)-average tropical cyclone counts (e.g., Chand et al., 2013 ; Evans & Allan, 1992 ; Goebbert & Leslie, 2010 ; Hastings, 1990 ; Liu & Chan, 2012 ; Ramsay, Camargo, & Kim, 2012 ; Ramsay, Leslie, Lamb, Richman, & Leplastrier, 2008 ; Ramsay, Richman, & Leslie, 2014 ; Solow & Nicholls, 1990 ; Werner & Holbrook, 2011 ). The decrease in tropical cyclone numbers during El Niño years in the Australian region has been linked to a geographical shift in activity, whereby an increase in storm frequency in the South Pacific, east of about 170°E, tends to offset the decrease in the Australian region (e.g., Basher & Zheng, 1995 ; Dowdy et al., 2012 ; Evans & Allan, 1992 ; Kuleshov, Qi, & Jones, 2008 ). Notable exceptions to the statistical relationship between ENSO and tropical cyclone frequency in the Australian region have occurred, for instance, during the La Niña events of 2010 / 2011 and 2011 / 2012 when average to below-average tropical cyclone numbers were observed. Moreover, the statistical relationship between ENSO and the annual number of tropical cyclones in the region has weakened notably since about the late 1990s (e.g., Dowdy, 2014 ; Ramsay, Richman, & Leslie, 2017 ), although the cause of this is currently unknown.

South Indian Basin

There is no robust relationship between ENSO and tropical cyclone counts in the South Indian Ocean basin when the entire basin is considered, but many studies have noted shifts in storm activity within the basin between different phases of ENSO. During El Niño events, more storms tend to form in the region west of roughly 75°E, whereas a greater frequency of storms have been observed to occur to the east of 75°E during La Niña episodes (e.g., Ho, Kim, Jeong, Kim, & Chen, 2006 ; Kuleshov & de Hoedt, 2003 ; Kuleshov et al., 2008 ; Ramsay et al., 2012 ). Ho et al. ( 2006 ) attributed the increase in tropical cyclone activity in the western half of the basin during El Niño to cyclonic atmospheric circulation anomalies there, whereas in the eastern part of the basin, anticyclonic circulation anomalies resulted in suppressed tropical cyclone formation rates. Contrary to the studies noted above, Jury et al. ( 1993 ) found no statistically significant relationship between the Southern Oscillation Index (SOI) and tropical cyclone numbers in the region west of 75°E. However, a statistical relationship with the QBO was observed such that when the QBO was in its easterly phase there tended to be higher-than-average numbers of tropical cyclones.

The year-to-year variability of tropical cyclone counts in the South Pacific region is strongly related to ENSO. There is a well-known northeastward shift in tropical cyclone genesis during El Niño such that anomalously high storm counts are observed to the east of about 170°E, affecting the islands of Polynesia including Fiji, Samoa, and the Cook Islands (e.g., Basher & Zheng, 1995 ; Chand & Walsh, 2009 ; Diamond, Lorrey, & Renwick, 2013 ; Dowdy et al., 2012 ; Ramsay et al., 2012 ; Revell & Goulter, 1986 ; Vincent et al., 2011 ). At the same time, the Australian region (90°E–160°E) experiences relatively few tropical cyclones during El Niño. Several large-scale environmental factors have been implicated to explain the northeast-southwest shift in storm activity between El Niño and La Niña episodes in the South Pacific, including variability in sea surface temperature, vertical wind shear (Dowdy et al., 2012 ), and lower tropospheric relative vorticity (Camargo, Emanuel, & Sobel, 2007 ). The interested reader is referred to Dowdy et al. ( 2012 ) for further discussion on large-scale environmental influences in the region.

Relatively few storms form in the North Indian Ocean compared to other Northern Hemisphere basins, with a seasonal average of three, partly due to the constraint posed by the Asian Continent extending into the latitudinal zone for tropical cyclone formation. The region has a unique seasonal cycle; when the Western North Pacific, Eastern North Pacific, and North Atlantic basins are close to their climatological peaks in storm activity during July–September, the North Indian Ocean basin experiences almost no tropical cyclones (only two formed during July–September in the 25-year period 1990–2014 ). The first peak in activity during May and June occurs shortly after the spring “predictability barrier” of ENSO, while the second seasonal peak in October to December is much closer to the canonical peak of ENSO. Both peaks are associated with the migration of the seasonal monsoon trough (e.g., Evan & Camargo, 2011 ; Gray, 1968 ; Li et al., 2013 ). Singh, Khan, and Rahman ( 2000 ) found modest, albeit significant, correlations between the SOI and tropical cyclone formation rates in the Bay of Bengal, with a greater frequency of events when the SOI was positive, but ENSO was found to have almost no impact on storm frequency in the Arabian Sea. Felton, Subrahmanyam, and Murty ( 2013 ) reported a statistically significant negative correlation between Niño 3.4 sea surface temperature and storm activity in the Bay of Bengal, which is consistent with the positive SOI relationship found by Singh et al. ( 2000 ).

ENSO has a large impact on the interannual variability of tropical cyclones in the Western North Pacific basin. Mirroring the South Pacific, there is shift toward the southeast (northwest) during El Niño (La Niña events). Chan ( 1985 ) was the first to point out this shift, finding that storm activity east of 150°E tended to be above normal during El Niño years; however, during the following year (after the El Niño event had lapsed) there was a basin-wide decrease in tropical cyclone activity. Several other studies have since confirmed the intra-basin shift in storm activity depending on ENSO phase (e.g., Chen, Weng, Yamazaki, & Kiehne, 1998 ; Chia & Ropelewski, 2002 ; Wang & Chan, 2002 ). The eastward migration of the main storm-formation region in El Niño years makes it possible for storms to track longer distances over the warm waters of the basin, and therefore they tend to be more intense than other years—particularly compared to La Niña years (e.g., Camargo & Sobel, 2005 ; Chan, 2007 ; Wang & Chan, 2002 ). Several atmospheric circulation anomalies have been proposed to explain the observed relationship between ENSO and tropical cyclone activity in the Western North Pacific, details of which can be found in review articles on the topic by Chan ( 2005 ) and Camargo, Sobel, Barnston, and Klotzbach ( 2010 ).

The QBO also has been suggested as a mode of variability that exerts some control on storm frequency in the region. Zhang, Zhang, and Wei ( 1994 ) found that when the QBO was in its westerly phase, enhanced tropical cyclone activity occurred. Chan ( 1995 ) confirmed the QBO influence, suggesting that increased tropical cyclone activity occurred when the lower-stratospheric winds were strengthening from the west. However, Camargo and Sobel ( 2010 ) found no significant correlations between QBO phase and the number of tropical cyclones in the basin for the period 1953–2008 . They also questioned the physical arguments used to explain the modulation of storm frequency by the QBO, finally concluding that QBO exerts no significant influence on tropical cyclones in the Western North Pacific or elsewhere.

Eastern North Pacific

The connection between large-scale climate drivers and the interannual variability of tropical cyclones in the Eastern North Pacific has remained somewhat elusive (e.g., Whitney & Hobgood, 1997 ), posing a challenge to seasonal forecasts in the region. Furthermore, the region has received relatively less attention in the scientific literature than its North Atlantic and Western North Pacific neighbors. Nevertheless, ENSO has been shown to exert some influence on the seasonal variability of storms in the region, in terms of both geographic location and intensity. Irwin and Davis ( 1999 ) found that during strong El Niño events, the formation region and associated storm tracks were displaced roughly 6° further west than the long-term mean longitude. This is consistent with the finding that El Niño supports a greater frequency of tropical cyclones in the central North Pacific (140°W to the Date Line), posing a greater threat to the Hawaiian Islands (Chu, 2004 ; Chu & Wang, 1997 ). Several other studies have confirmed this westward shift in genesis during El Niño years (e.g., Camargo, Robertson, Barnston, & Ghil, 2008 ; Wu & Chu, 2007 ). Recently, Jien, Gough, and Butler ( 2015 ) found a statistically significant difference in the frequency and intensity of storms in the western portion of the main development region (112°W to 140°W) depending on ENSO phase, with more frequent and more intense events occurring during El Niño years.

El Niño has been shown to have a direct influence on the intensity of storms in the Eastern North Pacific. Gray and Sheaffer ( 1991 ) found that intense tropical cyclones (i.e., wind speeds greater than 50 ms -1 ) were twice as likely to occur in El Niño years than La Niña years. In addition to the heat provided by the ocean surface, subsurface heating plays a critical role in tropical cyclone intensity and intensification by limiting the amount of cold upwelling induced by strong surface winds (e.g., Lin et al., 2013 ; Vincent, Emanuel, Lengaigne, Vialard, & Madec, 2014 ). While there exists a considerable lag between the canonical peak of ENSO during Boreal winter and peak storm season over the Eastern North Pacific (i.e., July-September), as well as a latitudinal mismatch between equatorial warming and the main storm development region, there is evidence to suggest that El Niño plays an important role in stimulating high-intensity storms by discharging subsurface ocean heat anomalies to the region some two to three seasons after the seasonal peak of ENSO (i.e., during Boreal winter) (Jin, Boucharel, & Lin, 2014 ).

North Atlantic

Internannual variability of tropical cyclones in the North Atlantic basin has a strong dependence on ENSO. Gray ( 1984 ) was the first to show that an El Niño event resulted in reduced storm activity over the western Atlantic for the season immediately following the onset of the El Niño. The reduction of storm activity was attributed to enhanced upper-level westerly winds. Indeed, during an El Niño year, vertical wind shear tends to be above average over a large part of the tropical Atlantic resulting in suppressed tropical cyclone formation. On the other hand, La Niña years are associated with reduced vertical wind shear in the region, which increases the likelihood of tropical cyclone formation (e.g., Goldenberg & Shapiro, 1996 ; Knaff, 1997 ). ENSO influences other environmental factors too, including increased moist static stability during El Niño years owing to increased upper-tropospheric temperatures in the tropics (Tang & Neelin, 2004 ). Aside from the variability in seasonal storm counts, ENSO modulates several other tropical cyclone metrics in the North Atlantic, including the number of hurricane days, the number of intense hurricanes (Gray, Landsea, Mielke, & Berry, 1993 ; Landsea, Pielke, Mestas-Nuñez, & Knaff, 1999 ), and the accumulated cyclone energy (ACE) (see Camargo et al., 2010 , for a review).

A significant statistical relationship existed between the QBO and Atlantic tropical cyclone activity for a period of the historical record. Gray ( 1984 ) originally proposed that basin-wide hurricane activity was 50–100% higher in the westerly phase of the QBO compared to its easterly phase. Several other studies have since implicated the QBO as a factor for modulating seasonal activity in the region, either for a particular season or a period of the historical record (see Camargo et al., 2010 , and Camargo & Sobel, 2010 , for a list of relevant studies). The relationship was statistically significant from about 1950 to 1983 , but not after that time, raising questions about the physical links underlying the strong statistical relationship in the earlier period (Camargo & Sobel, 2010 ). The QBO was removed as one of the predictors for seasonal hurricane activity by the Tropical Meteorology Project Group at Colorado State University (CSU) in 2007 .

Large-Scale Climatological Influences on Tropical Cyclone Formation and Tracks

It is clear from Figures 3 , 5 , and 6 that tropical cyclones tend to form in preferred regions of the globe. For instance, they rarely form poleward of 30°S in the Southern Hemisphere and poleward of 40°N in the Northern Hemisphere, and there is a dearth of formation points in the eastern South Pacific and South Atlantic basins. It is also apparent that storms only develop at some minimum distance away from the Equator.

Figure 5. (Top panel): figure from Gray ( 1968 , p. 671) showing the “designation of various tropical storm development regions and percentage of tropical storms occurring in each region relative to the global total. Numbers in parentheses are those of the average number of tropical storms occurring in each region per year. The 26.5°C isotherm for August in the Northern Hemisphere and January in the Southern Hemisphere is also shown.” ( © American Meteorological Society. Used with permission.) (Bottom panel): Tropical cyclone origin points for the period 1985–2014 from IBTrACS-WMO overlaid on climatological sea surface temperature data for August in the Northern Hemisphere and January in the Southern Hemisphere. The 26.5°C isotherm is outlined in blue.

Figure 6. The total number of tropical cyclones forming per 2.5° × 2.5° latitude-longitude bin in the Northern Hemisphere (top panel) and Southern Hemisphere (bottom panel) for the period 1985–2014 based on data from IBTrACS-WMO. Gray streamlines illustrate the typical surface wind patterns for August in the Northern Hemisphere and January in the Southern Hemisphere, following Gray ( 1968 ). The thick black dashed lines mark the positions of the climatological intertropical converegence zones (ITCZ) and monsoon troughs.

Palmen ( 1948 ) was the first to observe that tropical cyclones require a minimum threshold of sea surface temperature for formation and subsequent intensification. He stated that “hurricanes can be formed only in the oceanic regions outside the vicinity of the Equator where the surface water has a temperature above 26–27°C,” which is now commonly referred to as the “26.5°C (80°F) threshold,” and is pervasive in the tropical cyclone literature as well as many meteorological textbooks. In 1968 , William (“Bill”) Gray published his seminal paper, “Global View of the Origin of Tropical Disturbances and Storms” (Gray, 1968 ), which highlighted that the global mean climatology of tropical cyclones was linked to various large-scale features of the atmosphere and ocean. These features included the Equatorial Trough (and associated Intertropical Convergence Zone and Monsoon Trough) (Fig. 6 ), instability of the lower half of the troposphere, a minimum sea surface temperature threshold of 26.5°C, and the vertical shear of zonal wind between 850 hPa and 200 hPa. Once formed, tropical cyclones are “steered ” by predominant atmospheric flow patterns—notably by the easterly winds on the equatorward side of the subtropical highs (Fig. 6 ) such that they move generally from east to west during their early stages of life before drifting poleward and possibly “recurving ” with the midlatitude westerlies (Fig. 7 ). An exception to this track pattern occurs in the South Pacific basin, where storms more often than not move in a southeasterly direction (e.g., Chand et al., 2009 ; Knapp et al., 2010 ; Ramsay et al., 2012 ). Figure 3 compares Gray’s original 1968 tropical cyclone climatology with modern-day data. The general agreement between the global patterns of storm formation is fairly remarkable given that routine visible satellite pictures were not available until 1966 . The satellite-detected storm positions in the best track data since about 1970 have filled in a few gaps (Fig. 3 ), particularly in the central Pacific region, but even so Gray’s original work is a largely accurate depiction of global tropical cyclone formation patterns. In terms of the relative frequency of storms in each basin, the numbers have changed somewhat since 1968 , but Gray himself acknowledged that “with acquisition of more satellite and other conventional information, these percentages may have to be changed somewhat” (Gray, 1968 , p. 671). In hindsight, we can see that Gray ( 1968 ) overestimated the frequencies in the North Indian and Western North Pacific basins, while the formation rates in the Eastern North Pacific and North Atlantic regions were somewhat underestimated. Similarly, in the Southern Hemisphere, analyses of modern best track data indicate that Gray ( 1968 ) underestimated tropical cyclone occurrence in the Australian/South Pacific region by about 50%, consistent with the findings of Schreck et al. ( 2014 ).

Figure 7. Global tropical cyclone tracks for the period 1990–2010, color-coded by intensity on the Saffir-Simpson Hurricane Wind Scale. Tracks are from the IBTrACS-ALL best track dataset (i.e., both WMO and non-WMO data), and SSHWS categories are based on a 1-minute average wind speed.

In 1979 , Gray updated and refined his original 1968 paper using 20 years of storm data from 1958 to 1977 (Gray, 1979 ). The annual average global and hemispheric tropical cyclone counts have remained almost constant between the periods 1958–1977 and 1990–2014 , despite advances in satellite-based tracking techniques in the latter period. The global total average number of storms was 79.1 in 1958–1977 , compared to 78.5 in 1990–2014 . In the Northern and Southern hemispheres, the statistics for the same two periods are 54.6 ( 1958–1977 ) and 54.9 ( 1990–2014 ), and 24.5 ( 1958–1977 ) and 23.7 ( 1990–2014 ), respectively.

Seasonal Genesis Parameters

Arguably the most significant aspect of Gray ( 1979 ) was the introduction of six necessary, large-scale conditions for tropical cyclone genesis. According to Gray ( 1979 , pp. 167–168), “it appears that seasonal tropical cyclone frequency can be directly related on a climatological or seasonal basis to a combination of six physical parameters which will henceforth be referred to as primary climatological genesis parameters. These parameters are:

low-level relative vorticity ( ζ ‎ r ),

Coriolis parameter ( f ),

the inverse of the vertical shear, S z , of the horizontal wind between the lower and upper troposphere (1/ S z ),

“ocean thermal energy”—sea temperature excess above 26°C to a depth of 60 m ( E ),

vertical gradient of θ ‎ e between the surface and 500 mb (∂ θ ‎ e /∂ p ),

midde tropospheric relative humidity ( RH ).”

Gray hypothesized that tropical cyclone formation would be most favored where the product of the six parameters above are maximized, in terms of both geographic location and season. To that end, he posed a seasonal genesis parameter (s.g.p.), which combined the three “dynamic potential” parameters (1)–(3) with the three “thermodynamic potential” parameters (4)–(6). That is,

The interested reader is referred to Gray ( 1979 ) for further details on the physical relevance of the parameters, as well as their empirically derived thresholds, magnitudes, and units.

In an effort to further understand the relationship between tropical cyclone formation and the global climate system, Emanuel and Nolan ( 2004 ) refined Gray’s seasonal genesis parameter and developed the first Genesis Potential Index (GPI) based on modern day reanalysis data. The Emanuel and Nolan GPI, hereafter E-GPI, is defined by the equation

where η ‎ is the absolute vorticity in s -1 , Η ‎ is the relative humidity at 700 hPa in percent, PI is the potential intensity in ms -1 , and V shear is the magnitude of the vector wind difference between 850 and 200 hPa, in ms -1 . The major difference between Gray’s original seasonal genesis parameter and E-GPI is the utilization of potential intensity (PI) as the major thermodynamic control, rather than a threshold for sea surface temperature. Potential intensity, also referred to as maximum potential intensity (MPI), is a theoretical upper limit of the intensity of a tropical cyclone given sea surface temperature and environmental profiles of atmospheric temperature and humidity (Bister & Emanuel, 1998 ; Emanuel, 1986 ; Emanuel & Rotunno, 2011 ). The 26.5°C sea surface temperature threshold bounds the global tropical cyclone formation regions reasonably well in the current climate (Fig. 5 ), even on relatively short time scales (Dare & McBride, 2011 ), but theoretical and modeling studies suggest that this threshold will increase as the planet warms (e.g., Royer, Chauvin, Timbal, Araspin, & Grimal, 1998 ). PI, on the other hand, although often incorrectly viewed as a proxy for sea surface temperature, is a more robust thermodynamic parameter because it is sensitive to both local and global sea surface temperature changes (e.g., Ramsay & Sobel, 2011 ; Vecchi & Soden, 2007 ). The E-GPI has been shown to replicate some of the observed climatological characteristics of tropical cyclones, such as the seasonal cycles of individual basins (e.g., Camargo et al., 2007 ). Emanuel ( 2010 ) revised the E-GPI, replacing the relative humidity term with a variable associated with the saturation deficit of the mid-troposphere ( χ ‎). The saturation deficit in the mid-troposphere is expected to increase with global warming, assuming a constant relative humidity, which has implications for predicting how the rate of tropical cyclone formation will respond to such warming.

In 2011 , Tippett, Camargo, and Sobel ( 2011 ) proposed an alternative tropical cyclone genesis potential index (hereafter T-GPI) based on a Poisson regression model, in which the monthly number of storms in a given region was regressed against several monthly-averaged climate variables. Four important predictors emerged from the regression: (i) absolute voriticity at 850-hPa, (ii) 600-hPa relative humidity, (iii) relative SST, and (iv) the vertical wind shear between 850 and 200 hPa. The “relative SST,” which is the difference between the local SST and the tropical-mean SST, serves as a proxy for potential intensity (e.g., Ramsay & Sobel, 2011 ; Swanson, 2008 ; Vecchi & Soden, 2007 ). In developing the T-GPI, Tippett et al. ( 2011 ) found that the absolute vorticity term is important for tropical cyclone genesis at monthly time scales only up to a certain threshold (4 × 10 −5 s -1 ), beyond which other factors, such as thermodynamics or vertical shear, may be the primary rate-limiting influences.

The seasonal genesis indices described above are just a few of a number of tropical cyclone genesis indices developed for regional and global studies, inspired by Gray’s original seasonal genesis parameter (s.g.p; Gray, 1979 ). Other genesis indices include the Yearly Genesis Parameter (YGP; Royer et al., 1998 ), the Genesis Parameter (GP; DeMaria, Knaff, & Conell, 2001 ), and the Cyclone Genesis Index (CGI; Bruyere, Holland & Towler, 2012 ). The interested reader is referred to Menkes et al. ( 2012 ) for a review of seasonal genesis indices.

Tropical Cyclone Intensity

Tropical cyclone intensity estimates are an important component of the historical best track record and have significant implications for our understanding of the response of tropical cyclones to climate variability and change. Operational forecasting agencies rely heavily on satellite surveillance to locate storms and infer their intensities, particularly for regions where in situ measurements (from aircraft for example) are not available—which is everywhere but the Atlantic currently. The gold standard for inferring tropical cyclone intensity from satellite data is a subjective pattern recognition technique developed by NOAA scientist Vern Dvorak (Dvorak, 1975 ), known as the “Dvorak technique.” Meteorologists apply the technique in real time, based on available satellite imagery, and at the end of each season the intensity estimates are reassessed before being incorporated into the best track data. Despite efforts to merge the regional best track data into a single global archive (Knapp et al., 2010 ), there exists inherent inhomogeneities in the data due to (i) different wind-averaging periods used between agencies, (ii) changes in observational technology and analysis procedures over time, and (iii) the subjective nature of the Dvorak technique itself.

Satellite Observations of Tropical Cyclones and the Dvorak Technique

The first experimental satellite images of tropical cyclones came available in 1960 after the launch of the polar-orbiting weather satellite, TIROS-1. These images gave meteorologists the first snapshots of cloud patterns from above the earth’s surface. In 1961 , Hurricane Esther, the fifth storm of the season in the North Atlantic, became the first tropical cyclone to be discovered by weather satellite. By 1966 , the first global satellite monitoring system had begun, providing meteorologists with thousands of visible and infrared images of cloud formations, including tropical cyclones. A major advancement came in the 1970s with the introduction of geostationary satellites, which had the capability to continuously monitor the same area of the globe and provide tropical cyclone forecasters with an unprecedented regularity of cloud images. Three-hourly geostationary visible and infrared images became routinely available in 1978 (Harper, Stroud, McCormack, & West, 2008 ; Kossin et al., 2013 ). The 1980s and 1990s fostered even further development of satellite data, including sea surface temperature measurements and cloud drift winds (Velden & Hawkins, 2010 ).

As satellite technology was rapidly advancing through the 1970s and 1980s, NOAA scientist Vern Dvorak was at the same time working on a technique to estimate tropical cyclone intensities based on satellite-derived cloud patterns. The so-called Dvorak Technique was first summarized in a paper by Dvorak himself in 1975 (Dvorak, 1975 ) and since then has become one of the most famous and widely applied techniques in meteorology. It has undergone several modifications since its first inception, particularly during the 1980s (Dvorak, 1984 ) with the implementation of the Enhanced Infra-Red (EIR) Dvorak Technique and the 2000s (Velden et al., 2006 ) with the development of the Advanced Dvorak Technique (ADT); still, the core principles of the technique remain to this day. In practice, the operational meteorologist follows a set of cloud pattern recognition rules to determine a “T-number,” which is subsequently converted to a CI (Current Intensity) number, before finally being translated into a maximum sustained surface wind speed (see Velden et al., 2006 , for an example of CI number to MSSW speed conversions in the Atlantic and Western North Pacific basins). As Velden et al. ( 2006 ) point out, a remarkable aspect of the technique is its absolute accuracy (50% of the MSSW speed estimates are within 5 knots of in situ reconnaissance aircraft measurements) combined with its internal consistency. The interested reader is referred to the review article by Velden et al. ( 2006 ) for a detailed history and discussion of the Dvorak Technique.

Climatological Aspects of Tropical Cyclone Lifetime Maximum Intensity

The global distribution of tropical cyclone lifetime maximum intensity (LMI) locations for the period 1985–2014 , based on IBTrACS-WMO data, is shown in Figure 8 . The LMI is defined as the first point at which a tropical cyclone reaches its maximum MSSW speed over the course of its life. It is apparent from Figure 8 that the most intense storms, those of category 4 and 5 on the Saffir-Simpson hurricane wind scale (SSHWS), reach their LMI at latitudes further equatorward than storms of lesser intensity. In the Northern Hemisphere, the mean latitudes of LMI for Category 5 and Category 4 systems are 17.2°N and 17.7°N, respectively, compared to a mean latitude of 20.4°N when all systems are taken into account. In the Southern Hemisphere, the mean latitude of LMI for Category 5 storms is 13.8°S, compared to a latitude of 17.4°S for all storms. The mean latitude of LMI has been shown to vary from basin to basin (Kossin, Emanuel, & Vecchi, 2014 ), with storms in the North Atlantic basin reaching their LMI at considerably higher latitudes compared to other tropical cyclone basins. Globally, 42% of tropical cyclones remain at or below tropical storm intensity (30 kt < LMI < 56 kt) on the SSHWS, with the remaining 58% reaching hurricane intensity (i.e., LMI >= 56 kt) (the majority of those being Category 1 storms). Of the 1,373 systems that reached hurricane intensity between 1985 and 2014 , 458 were Category 1, (19%), 269 were Category 2 (11%), 297 were Category 3 (13%), 308 were Category 4 (13%), and 41 were Category 5 (2%). The hemispheric distributions of LMI are broadly similar to the total global distribution (Fig. 9 ), but there exists considerable basin-to-basin variability. An anomaly that stands out in Figures 8 and 9 is the lack of high-intensity tropical cyclones in the Western North Pacific, particularly Category 5 storms. The region is home to the highest annual number of tropical cyclones in the world as well as to the Western Pacific warm pool (Fig. 5 ), so it stands to reason that it should support a greater number of extreme tropical cyclones than is apparent in Figures 8 and 9 . The discrepancy arises primarily from interagency differences in the best track data. The Japanese Meteorological Agency (JMA), which provides data to the WMO version of IBTrACS, documents systematically low LMIs compared to the Joint Typhoon Warning Center (JTWC). For example, Typhoon Haiyan in November of 2013 (Fig. 10 ) reached a maximum intensity of 125 knots (10-minute average) according to the JMA (Table 1 ), whereas the JTWC estimated its peak intensity to be 170 knots (1-minute average; equivalent to 10-minute average 150 knots). Such wind speed discrepancies between the JMA and the JTWC are known to affect systems with LMIs exceeding roughly 96 knots (Knapp & Kruk, 2010 ; Schreck et al., 2014 ). On the other hand, best track LMIs in the Australian Bureau of Meteorology tend to be systematically higher than in the JTWC best track data.

Figure 8. The locations of lifetime maximum intensities (LMI) of tropical cyclones for the period 1985–2014. LMI is color-coded according to category on the Saffir-Simpson Hurricane Wind Scale.

Figure 9. The distribution of lifetime maximum intensities (LMI) of tropical cyclones for all regions (Global), Northern Hemisphere (NH), Southern Hemisphere (SH), and each of the individual basins. The shaded rectangles show the interquartile range of LMIs, with the corresponding median value indicated by the thick horizontal line. The extreme upper and lower horizontal lines show the minimum and maximum LMI for each region, respectively (excluding outliers, which are indicated by open circles). The gray horizontal lines indicate intensity thresholds according to the Saffir-Simpson Hurricane Wind Scale, noting that the thresholds have been scaled to be consistent with 10-minute average MSSW speed data.

A list of the top 10 most intense tropical cyclones for each basin between 1985 and 2014 , based on IBTrACS-WMO data, is provided in Table 1 . Note that several new LMI records have since been set in the Eastern North Pacific basin (Hurricane Patricia), the South Indian basin (Very Intense Tropical Cyclone Fantala), and the South Pacific basin (Severe Tropical Cyclone Pam).

Table 1. Top 10 Most Intense Tropical Cyclones During the Period 1985–2014 for Each Basin a

a Intensity is ranked first by 10-minute average wind speed and then by minimum central pressure.

A dagger symbol (†) indicates that the storm no longer holds the record for the most intense tropical cyclone in its basin (e.g., Hurricane Patricia in 2015 became the strongest on record in the ENP).

An asterisk (*) indicates that the maximum intensity was shared with at least one other storm in the best track data for the same basin.

b NA, North Atlantic; ENP, Eastern North Pacific; WNP, Western North Pacific; NI, North Indian; SI, South Indian; AUS, Australian region; SP, South Pacific.

Source : IBTrACS-WMO data https://www.ncdc.noaa.gov/ibtracs/index.php?name=wmo-data .

Figure 10. Typhoon Haiyan at peak intensity on November 7, 2013, with 10-minute maximum sustained winds of 230 km h -1 (145 mph).

Current Roadblocks to Our Understanding

Knowledge of the global distribution of tropical cyclones, including regional variability and trends, has progressed significantly since the advent of satellites in the 1960s. Yet despite rapid advancements in observational technology, the pioneering studies of tropical cyclone climatology, such as Gray ( 1968 ), provide a remarkably accurate depiction of the global spatial patterns of tropical cyclone formation (Fig. 3 ).

A significant roadblock to our understanding of past variability and trends in tropical cyclone activity resides in temporal heterogeneities present within the historical best track data. Changes in observational technology and analysis procedures over time have compromised the quality of the global best track data, particularly prior to the early 1980s. These heterogeneities affect not only the historical intensities of storms, but also regional and global storm counts because tropical cyclone classification depends on the maximum sustained surface winds exceeding a certain threshold. Furthermore, prior to satellite data, the chance of a storm being missed over the open ocean was much greater because such detection required that a ship encounter the tropical cyclone directly (e.g., Vecchi & Knutson, 2011 ). Even within the period of most reliable data since the mid-1980s, temporal heterogeneities exist due to, for example, changes in satellite viewing angles (Kossin et al., 2013 ), as well as interagency differences in wind-averaging periods and the subjective nature of the Dvorak technique itself. In an attempt to resolve some of these issues, considerable efforts have been invested into reanalysing historical meteorological data with the benefit of hindsight (e.g., Hagen, Strahan-Sakoskie, & Luckett, 2012 ; Harper et al., 2008 ; Hennon, 2012 ; Landsea, 2007 ; Landsea, Vecchi, Bengtsson, & Knutson, 2010 ; Landsea et al., 2012 , 2014 ), including the development of temporally consistent, satellite-based, global tropical cyclone intensity data (e.g., Kossin, Knapp, Vimont, Murnane, & Harper, 2007 ; Kossin et al., 2013 ), which give a more accurate depiction of global and regional trends—particularly in relation to potential climate change impacts. For example, there has been a significant poleward migration of the mean latitude at which tropical cyclones attain their LMI during the period 1982–2012 (Kossin et al., 2014 ), which is one of the more robust results linking tropical cyclone intensity to climate change and variability.

On a more fundamental note, a satisfactory answer to the question of what sets the annual global rate of tropical cyclone formation, roughly 80 per year, has thus far evaded climate scientists. Several empirical relationships have been derived to relate tropical cyclone formation to large-scale climate variables, such as genesis potential indices, but there is to date no established theory relating tropical cyclone formation rate to climate. To understand how regional and global tropical cyclone activity will change with a changing climate, it is paramount that we first understand what sets the rate of tropical cyclone formation in the current climate.

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Original research article, a hypothesis and a case-study projection of an influence of mjo modulation on boreal-summer tropical cyclogenesis in a warmer climate with a global non-hydrostatic model: a transition toward the central pacific.

• 1 Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama-Shi, Kanagawa, Japan
• 2 Center for Earth Surface System, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa-Shi, Chiba, Japan
• 3 Computational Climate Science Research Team, Advanced Institute for Computational Science/RIKEN, Kobe, Hyogo, Japan

The eastward shift of the enhanced activity of tropical cyclone to the central Pacific is a robust projection result for a future warmer climate, and is shared by most of the state-of-the-art climate models. The shift has been argued to originate from the underlying El-Ñino like sea surface temperature (SST) forcing. This study explores the possibility that the change of the activity of the Madden–Julian Oscillation (MJO) can be an additional, if not alternative, contributor to the shift, using the dataset of Yamada et al. (2010) from a global non-hydrostatic 14-km grid mesh time-slice experiment for a boreal-summer case. Within the case-study framework, we develop the hypothesis that an eastward shift of the high-activity area of the MJO, as manifested itself as the significant intra-seasonal modulation of the enhanced precipitation, is associated with the increased tropical cyclogenesis potential over the North central Pacific by regulating cyclonic relative vorticity and vertical shear. In contrast, the North Indian Ocean and maritime continent undergo relatively diminished genesis potential. An implication is that uncertainty in the future tropical cyclogenesis in some part of the Pacific and other ocean basins could be reduced if projection of the MJO and its connection with the underlying SST environment can be better understood and constrained by the improvement of climate models.

Introduction

The regional change of tropical cyclones in future warmer climate has been a highly debated area of research. Even the sign of change in cyclone frequency differs from one model to another based on well-coordinated recent model datasets from Coupled Model Intercomparison Project (CMIP) 3 and 5 ( Knutson et al., 2010 ; Camargo, 2013 ; Emanuel, 2013 ; Tory et al., 2013 ). However, the state-of-the art hydrostatic general circulation models all project an overall increase of genesis in the central Pacific, compared to present-day climatic conditions ( Li et al., 2010 ; Murakami et al., 2012 ; Zhao and Held, 2012 ). This change was mostly ascribed to the underlying El-Ñino-type sea surface temperature (SST) forcing which was specified as a warmer climate condition. Notably, a time-slice experiment with the global non-hydrostatic model also projected a similar change of tropical cyclone activity ( Yamada et al., 2010 ; Y2010). It remains unclear however how the change of the tropical cyclogenesis would be influenced by the associated changes of atmospheric phenomena such as the Madden–Julian Oscillation (MJO, Madden and Julian, 1971 , 1972 ). Li et al. (2010) indicated that the enhanced tropical cyclone activity over the central Pacific region can be related to an increased variance of tropical synoptic-scale perturbations. This short article sheds a new light on this problem by focusing on a link between the change in the role and horizontal distribution of the MJO and the tropical cyclogenesis over the North central Pacific under a warmer climate state based on the experiment reported by Yamada et al. (2010) . Note that the boreal-summer season is the focus of this research.

The model used is the Non-hydrostatic ICosahedral Atmospheric Model (NICAM, Tomita and Satoh, 2004 ; Satoh et al., 2008 ), a global model that is capable of calculating meso-scale convection, which is an essential building block of tropical convection, but a most elusive element in traditional hydrostatic models. With these benefits, NICAM simulations captured boreal winter- ( Miura et al., 2007 ; Fudeyasu et al., 2008 ), spring- ( Taniguchi et al., 2010 ), and summer-time ( Oouchi et al., 2009 , 2012 ; Satoh et al., 2012 ) MJO events and associated tropical cyclogenesis as well. The application of NICAM to research on the future change of the MJO and tropical cyclogenesis draws strongly upon these successful case studies. This study does not go beyond the case-study framework. As the model needs large computational resources, we were required to make some compromises with respect to various aspects, e.g., temporal duration and the size of ensembles in order to perform the integration necessary to obtain a greater statistical validity for intra-seasonal phenomenon such as MJO. The time integration is 5 months each of present and future experiments. Among various issues of MJO, this study focuses on the geographical change of the MJO activity that can be related to tropical cyclogenesis. The design of the experiment is explained in section Experimental Design, which is followed by the presentation of the results in section Results. Section Summary and Remarks concludes with a summary and further remarks.

Experimental Design

The NICAM experiments are performed using a grid spacing of around 14-km. The method used is a time-slice experiment ( Bengtsson et al., 1996 ), and details are explained in Yamada et al. (2010) . To be brief, the model is integrated over the five (JJASO) and six (MJJASO)—months period for present-climate (PRESENT) and future (FUTURE) experiment, respectively. The SST in PRESENT is derived from the NOAA Optimum Interporation (OI) SST V2 dataset for 2004 ( Reynolds et al., 2002 ). In FUTURE, the model was spun up during all of May, which is excluded from the analysis. The FUTURE SST forcing is created by adding the differences between PRESENT (1979–2003) and FUTURE (2075–2099) onto the PRESENT SST with the dataset of the World Climate Research Program Coupled Model Inter-comparison Project phase 3 (CMIP3), following the method of Mizuta et al. (2008) . The forcing has an El-Ñino like horizontal pattern (Y2010, Figure 1 ) which, as argued later, affects the interpretation of the results. The projection of tropical cyclone change in the same suite of data is reported in Y2010 along with its comparison with a downscaling method ( Emanuel et al., 2010 ). The tracking methodology of tropical cyclone follows Yamada et al. (2010) . The observational dataset is from the Unisys Corporation ( http://weather.unisys.com/hurricane/ ).

Figure 1. (A) Hovmoller plots of precipitation averaged over 5° S–5° N [mm day −1 ] for PRESENT. (B) Same as (A) except for FUTURE. (C) Blue bars: accumulated precipitation amount [mm] summed over the date with higher precipitation rate (more than 8 mm day −1 ) during JJASO for PRESENT; bottom numerals: tropical cyclone counts for western Pacific, Indian Ocean, Pacific, and Atlantic. Pacific area are divided as 100°–150° E, 150° E–160° W, and 160°–90° W with slash marks, (D) Same as (C) .

We first focus on a comparison of the time series of equatorial precipitation, zonal distribution of its accumulated amount, and zonal-vertical circulation in the tropical region between PRESENT and FUTURE (Figure 1 ). A significant future change in precipitation is the increased precipitation near the dateline, a location which closely coincides with the underlying largest SST forcing (Figures S1A,B). The precipitation undergoes a marked intra-seasonal modulation in FUTURE. Consequently, the main area of upwelling for JJASO is located more eastwardly in FUTURE, and is characterized by the intensified vertical shear in the western and eastern branches with respect to its center (Figures S1C,D).

The change in the zonal circulation pattern is also confirmed by both the Walker Circulation Index (WCI, Wang, 2002 ) and also the tropospheric (from the surface to 150 hPa) moisture convergence flux (MFlx) averaged over the Indian ocean (60–100°E) (Figure S2). The Walker circulation is driven by the temperature difference in the underlying SST along the equatorial Pacific, and the WCI measures the vertical velocity anomaly difference between the eastern and western Pacific region at the 500-hPa level (See Wang, 2002 for a detailed definition).

An overall comparison reveals that throughout the simulation periods, the sign of the temporal WCI variation is almost in phase with that of MFlx in PRESENT, and out of phase in FUTURE. In other words, the zonal circulation pattern over the Indian-Ocean to the entire Pacific sector changes in FUTURE from bimodal to unimodal consisting respectively, of upward/downward motions in the central Pacific and downward/upward motions in the western Pacific and the Indian Ocean.

Figures 1C,D is annotated with tropical cyclone counts during JJASO. Each of the three numbers separated by slash marks are the Pacific counts for 100–150° E, 150 o E–160 o W, and 160–90 o W, respectively. The total number of tropical cyclones decreases in FUTURE, as previously shown by Y2010, and consistent with other previous studies. However, an increase in the FUTURE count is evident over the central Pacific (150 o E–160 o W), in contrast with a decrease over the western side (100–150 o E), showing an eastward shift of tropical cyclogenesis in FUTURE. Figure 1B shows the intra-seasonal variation of precipitation over the central Pacific. This implies that the increase in tropical cyclones over the North central Pacific is related not solely to the change in stationary flows associated with the ENSO-type SST response, but also to changes in intra-seasonal oscillations, i.e., the MJO. It is unclear whether any intra-seasonal variability of precipitation over the central Pacific is associated with the MJO. The next question is in what way the active areas of the MJO and tropical cyclogenesis change in association with these changes in climate state.

The change in MJO activity can be seen in Figure 2 displaying the Hovmoller plots of zonal velocity anomaly at 200 hPa ( A,B ), and Real-time Multivariate (RMM) MJO indices ( C,D ) based on Wheeler and Hendon (2004) (WH04). The indices elucidate the behaviors of MJO over the different longitudes. They are based on extended empirical orthogonal function analysis of zonal velocity anomalies at 200 and 850 hPa in addition to the outgoing longwave radiation (OLR) anomaly derived from the daily outputs. To create anomalies, the climatological mean of the reanalysis dataset for the period 1979–2001 is subtracted from the raw output using NCEP/NCAR daily reanalysis ( Kalnay et al., 1996 ) for velocities and NOAA for OLR. Each anomaly is then divided by its longitudinally-averaged normalization factor (as computed in WH04 to be 15.1 Wm −2 for OLR, 1.81 ms −1 for 850 hPa zonal wind, and 4.81 ms −1 for 200 hPa zonal wind), and then these are projected onto the WH04 EOFs. Estimating the applicability of the indices to the future climate dataset is an important subject for future studies.

Figure 2. (A) Hovmoller plots of zonal velocity anomaly at 200 hPa from the time average over JJASO [ms −1 ] for PRESENT. (B) Same as (A) except for FUTURE. (C) PC1–PC2 phase-space points of JJASO MJO activity for PRESENT based on the method of Wheeler and Hendon (2004) . For each of eight equal-angled phase-space categories, the approximate locations of the enhanced convective signal of MJO are labeled. (D) Same as (C) except for FUTURE.

There are eight phases for categorizing the convectively active signal of the MJO (PH1–8), each of which roughly corresponds to the area of active MJO center. Overall, the eastward movement of the velocity is seen both in PRESENT and FUTURE (Figures 2A,B ), and clear amplifications of its eastward movement are detected in FUTURE (e.g., earlier in August over 120–60 o W, and in late August over 120 o E–180 o ). On the RMM phase space, the eastward movement of the MJO can be seen through the traces moving counter-clockwise across the corresponding geographical location. Figure 2C indicates the presence of an eastward movement from early June to early July which corresponds to the MJO case discussed by Oouchi et al. (2009) . During July and August, no clear eastward movement is present compared to the observations (URL: http://cawcr.gov.au/staff/mwheeler/maproom/RMM/phasediag/pd.2004.6.1.gif ), but it is followed by a stronger signal in October across PH6 and 7, being almost comparable to a western Pacific MJO signal in the early October ( Nakazawa, 2006 ). As we do not expect the model to be skillful at simulating the behavior of the MJO beyond 20 days or so ( Vitart, 2009 ), the apparent similarity of the signals later than mid-July is not examined further here. A comparison between PRESENT and FUTURE reveals a series of suppressed signals during June and July, while a stronger signal is present in August corresponding to the one mentioned above. Among PH1–8, the amplitude in FUTURE increases relatively in PH6 and 7, compared to PH2–4. These results are consistent with the intuitive impressions obtained by the Hovmoller diagrams shown by Figure 1 .

To see a rough linkage between the change of the MJO and the environment of tropical cyclogenesis, Figures 3A,B illustrates the large RMM phase (LRP, eastward propagation only) which we define as the date when the difference of the maximum amplitude of MJO index between the consecutive 6-days period is larger than 1.2 (red triangles) or between 0.8 and 1.2 (blue triangles); ( C,D ) are the meridional-time plot of genesis potential density (GPD) defined as the ratio of genesis potential index (GPI; Emanuel and Nolan, 2004 ) with respect to the entire period and PH regions. In this study, GPI is calculated for each of the following areas for major ocean basins to facilitate discussion: (Indian Ocean, 45–90° E; maritime continent, 90–110° E; Western Pacific, 100–150° E; Central Pacific, 150° E–160° W; Eastern Pacific and Atlantic, 160–20° W. In PRESENT, we cannot see close correspondence between the LRP in excess of 1.2 and a higher GPD. In other words, GPD is not necessarily contributed to by the growth of RMM in PRESENT. On the other hand, in FUTURE the correspondence becomes stronger over the central Pacific (PH6 and 7), and to some degree over the Eastern Pacific and Atlantic, suggesting a closer link between the MJO's amplification, and higher GPD over the central Pacific. Interestingly, the higher GPD is more dominant over the central Pacific and the western hemisphere than it is elsewhere in FUTURE. This may be associated with the bimodal/unimodal circulation patterns in PRESENT/FUTURE (Figure S1).

Figure 3. (A) The large RMM phases defined as the date when the difference of the maximum RMM amplitude between the consecutive 6-days period is larger than 1.2 (red) and 0.8 (blue) for PRESENT. (B) Same as (A) except for FUTURE. (C) Time series of genesis potential index ( Emanuel and Nolan, 2004 ) density [%] for PRESENT for the following regions: Indian Ocean, 45–90° E; maritime continent, 90–110° E; Western Pacific, 100–150° E; Central Pacific, 150°E–160°W; Eastern Pacific and Atlantic, 160–20° W. The density is with respect to whole defined regions and period (JJASO). (D) Same as (C) except for FUTURE.

To investigate the link between MJO and tropical cyclogenesis potential in the North central Pacific, the meridional-time plot of relative vorticity anomaly at 850 hPa over 150 o E–160 o W is shown in the upper panel of Figure 4A for PRESENT and ( B ) for FUTURE. The “central” Pacific region is selected, as defined in the tropical cyclone count in the slash-marked values in Figures 1A,B . The bottom sub-panels ( A,B ) plot the time series of the relative vorticity, RMM amplitude (larger than 0.8), and vertical shear averaged over 0–10° N and the longitudinal region. A comparison between PRESENT and FUTURE reveals that increased cylonic vorticity is more notable in FUTURE. The increase closely coincides with that of the RMM amplitude and also the decrease of the vertical shear, and these features become more pronounced in FUTURE. This suggests that the propagation of the MJO and associated enhancement of vorticity and weakening of vertical shear can contribute positively to tropical cyclogenesis over the North central Pacific region in FUTURE. The tropical cyclogenesis locations are plotted in the upper panels of Figures 4A,B . We can see that the genesis in FUTURE occurs at the timing of the larger RMM phase, and it is associated with larger values of cyclonic vorticity and negative anomaly of vertical shear more clearly than PRESENT. A clear case occurs during first August–mid September. These results suggest a possible connection between the eastward shift of the MJO location and that of tropical cyclogenesis over the North central Pacific in FUTURE.

Figure 4. (A) Top; Meridional-time plot of relative vorticity at 850 hPa averaged over 150° E–160° W (the “central” Pacific region as defined in the tropical cyclone count in the slash-marked values in Figures 1C,D ) for PRESENT, Bottom; the time series of relative vorticity (multiplied by arbitrary factor), RMM amplitude (larger than 0.8), and vertical shear averaged over the above longitudinal region and 0–10° N. The tropical cyclone marks indicate the genesis points in the region. (B) Same as (A) except for FUTURE.

Summary and Remarks

The eastward shift of the enhanced Pacific tropical cyclone activity—from the western Pacific to the central Pacific—under a future warmer climate has been a widely projected result in the global climate models, including the global non-hydrostatic model (Y2010). However, no previous studies have discussed its association with the change of the MJO. This study proposes a hypothesis for this question within a boreal summer case-study framework of Y2010 using the global non-hydrostatic model NICAM. Although the simulation is too short to consider inter-annual variability, numerical results show a common signal of decrease in the total number of tropical cyclones in FUTURE (Y2010). In our result, the increase in tropical cyclones is seen over the North central Pacific; this motivates us to further investigate what causes the enhancement of tropical cyclone activity over the North central Pacific in FUTURE. We found that the simulated MJO becomes active in August in FUTURE, and tropical cyclones are more generated associated with this active MJO.

Our study has the following implications: (1) an eastward shift of the higher activity area of the MJO reaching the central Pacific with its likely association with the underlying SST forcing, enhanced precipitation and Walker circulation; (2) relatively weakened activity over the tropical warm pool across the Indian Ocean to maritime continent; (3) an increase in tropical cyclogenesis potential over the North central Pacific associated to some extent with the propagation of MJO that favorably controls the vertical shear and relative vorticity, and (4) relatively suppressed tropical cyclogenesis potential over the North Indian Ocean and maritime continent, whose association with MJO activity relative to other factors remains to be substantiated. The finding (1) supports the prevailing notion that the warmer SST condition helps sustain or enhance the development of MJO, and is consistent with the observational evidence that convective activity and the atmospheric response to SST propagates more eastwardly into the central Pacific in El-Ñino years ( Dunkerton and Crum, 1995 ; Hendon et al., 1999 ; Kessler, 2001 ). It remains to be completely clarified whether TC genesis under such a condition would be more likely to appear under either or both of the effects of MJO and SST, which may not be competing depending on the time scale of interest.

In the context of the present climate, there is a large body of observational studies on the possible modulation of tropical cyclones by the MJO over the global basins (e.g., Camargo et al., 2009 ), the western Pacific (e.g., Nakazawa, 1986 ; Liebmann et al., 1994 ; Kim et al., 2008 ), and the Atlantic ( Klotzbach, 2010 ). This paper focuses on the potential increase of tropical cyclones through the change of MJO-associated synoptic-scale environmental fields including the parameters relevant to the modulation associated with the MJO (e.g., intensified relative vorticity, and moderated vertical shear, Camargo et al., 2009 ) over the central Pacific. Clarification of the mechanism behind the causal relationship between the activation of the MJO and the net increase of TC awaits further study. One might argue that such a straightforward extrapolation of “present-climate” analogy to future condition needs to be carefully considered. There can be other factors responsible for increase of the total number of TCs that may differ from one basin to another depending on the different background states between future and present climate conditions, and this question is left for future investigation. It should also be noted that much longer-term simulation is necessary to clarify how TC modulation is affected by different MJO-phases (both active- and inactive- MJO periods), in addition to the MJO-active case presented here. These concerns need to be addressed in future.

While our results suggest that our understanding of changes in Pacific tropical cyclogenesis in a future warmer climate may be enhanced by further research on the link between MJO and associated tropical cyclogenesis, much work remains to be done. As only a very few MJO events are simulated in our study, the generality of the results cannot be proven in any climatological sense, and this needs to be investigated in future with longer-period simulations. This study does not consider future changes of the MJO (and evaluation of the MJO index) per se , which would need a larger sample size with extended simulation period, and which could bring new challenges of its own. Additionally, some cautions need to be kept in mind to ensure progress in a suitable direction. Pacific tropical cyclogenesis is known to be substantially affected by other tropical disturbances in addition to the MJO, including equatorial waves (e.g., Li, 2012 ). Even when the MJO apparently controls cyclogenesis, other disturbances can play roles in some way or another, associated with or independent of the presence of the MJO. In this article, the effects of the other disturbances are ignored in favor of the MJO, assuming that the MJO would exert the largest scale first-order control. Identifying the relative effects of the other disturbances will be a priority for future investigation. We also did not address the exact role of the MJO in controlling the frequency: whether it generates or modulates the tropical cyclone activity, and what sub-synoptic pathway is present to the emergent tropical cyclone. Future work should explore this issue. Given the shortness of the time integration spanning 5 months, this study is viewed as a case study for a boreal summer season. We plan to extend the experiment across the seasons to evaluate the statistical reliability of the findings. In the near future, we also plan to expand the temporal boundaries of the research as we get access to greater computing resources via the K-computer ( Yokokawa et al., 2011 ).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author thanks development members of NICAM team. The numerical experiments were performed on the Earth Simulator of JAMSTEC under the framework of KAKUSHIN project funded by the Ministry of Education, Culture, Sports, Science, and Technology, Japan. The CMIP3 sea surface temperature and sea ice concentration dataset was provided by the Meteorological Research Institute. The comments from reviewers helped improve the original manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/feart.2014.00001/abstract

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Keywords: central-Pacific tropical cyclone, future MJO, global non-hydrostatic projection, model-derived hypothesis, seamless weather projection

Citation: Oouchi K, Satoh M, Yamada Y, Tomita H and Sugi M (2014) A hypothesis and a case-study projection of an influence of MJO modulation on boreal-summer tropical cyclogenesis in a warmer climate with a global non-hydrostatic model: a transition toward the central Pacific? Front. Earth Sci . 2 :1. doi: 10.3389/feart.2014.00001

Received: 04 December 2013; Accepted: 28 January 2014; Published online: 18 February 2014.

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Copyright © 2014 Oouchi, Satoh, Yamada, Tomita and Sugi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kazuyoshi Oouchi, Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showamachi, Kanazawa-ku, Yokohama-Shi, Kanagawa 236-0001, Japan e-mail: [email protected]

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• Published: 18 December 2023

Impacts of tropical cyclones on the global water budget

• Albenis Pérez-Alarcón   ORCID: orcid.org/0000-0002-9454-2331 1 , 2 ,
• Patricia Coll-Hidalgo 1 ,
• José C. Fernández-Alvarez 1 , 2 ,
• Ricardo M. Trigo   ORCID: orcid.org/0000-0002-4183-9852 3 , 4 ,
• Raquel Nieto   ORCID: orcid.org/0000-0002-8984-0959 1 &
• Luis Gimeno   ORCID: orcid.org/0000-0002-0778-3605 1  

npj Climate and Atmospheric Science volume  6 , Article number:  212 ( 2023 ) Cite this article

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Tropical cyclones (TCs) require substantial amounts of moisture for their genesis and development, acting as important moisture drivers from the ocean to land and from tropical to subtropical and extratropical regions. Quantifying anomalous moisture transport related to TCs is crucial for understanding long-term TC-induced changes in the global hydrological cycle. Our results highlight that, in terms of the global water budget, TCs enhance moisture transport from evaporative regions and precipitation over sink regions, leading to predominantly anomalous positive surface freshwater flux areas over the tropics and more regionally concentrated negative areas over the Intertropical Convergence Zone. Furthermore, we detected seasonal variability in the impact of TC on the hydrological cycle, which is closely related to the annual and seasonal TC frequency. Our analysis also revealed a global statistically significant drop (~40 mm year −1 ) in TC-induced surface freshwater fluxes from 1980 to 2018 in response to the increasing sea surface temperature and slightly decrease in global TC frequency and lifetime in the last two decades. These findings have important implications for predicting the impacts of TCs on the hydrological cycle under global warming conditions.

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Introduction.

Tropical cyclones (TCs) are critical for transferring moisture from the tropics to mid-latitudes and high latitudes 1 , 2 and from the ocean to land 3 , 4 , thereby influencing weather patterns in tropical and subtropical regions worldwide [e.g. ref. 5 ] and playing a crucial role in the atmospheric branch of the global hydrological cycle 3 , 4 , 6 , 7 . While relevant in these roles, the contribution of TCs to poleward water vapour transport from tropical to extratropical regions across the mid-latitudes globally is substantially smaller than that of atmospheric rivers 8 . Nonetheless, intense convective activity during TCs induces a vast amount of water vapour that is redistributed across the atmosphere and can produce notable precipitation totals in terrestrial regions 6 , 7 , providing moisture to areas that may experience dry conditions 9 . However, impacts of TCs are associated with substantial environmental disturbances as well as socioeconomic and human losses caused by extreme compound events, such as strong winds, heavy rainfall, storm surges, flash flooding, and landslide 10 , 11 .

Atmospheric moisture availability is crucial for TC genesis and development 12 . Indeed, modelling and theoretical studies have demonstrated that an increase (decrease) in mid-level water vapour content enhances (inhibits) TC intensification 13 , 14 . Furthermore, understanding the origin of moisture for precipitation produced by TCs is key to significantly assisting in risk analysis and mitigating the often strong associated impacts. Recently, Pérez-Alarcón et al. 15 , 16 , 17 revealed that precipitating moisture was predominantly highest from sources close to the TC positions. Likewise, large-scale atmospheric circulation patterns in each region drove the moisture towards the TC locations.

Improving our knowledge of moisture transport is crucial for investigating extreme precipitation events 18 , 19 . Anomalous moisture transport caused by changes in large-scale circulation is a key factor in long-term changes in extreme precipitation 20 . Thus, TCs contribute significantly to onshore moisture transport. Because moisture and heat fluxes from the warm sea surface are the principal fuels for TCs 13 , previous studies have been mainly focused on the TC water budget 21 , 22 , 23 , 24 , 25 , 26 , the origin of moisture for TCs precipitation 15 , 16 , 17 , 27 , the contribution of TCs to monthly and annual precipitation totals in tropical and subtropical latitudes 6 , 7 or the TC-related onshore moisture transport 3 , 4 . For example, moisture convergence is the main contributor to TC rainfall 24 , 26 ; TCs are responsible for approximately 14–19% of the net onshore moisture driven towards North America 3 and their precipitation represents a significant fraction of the annual precipitation totals at regional, continental and even global scales 4 , 6 . Additionally, part of the moisture gained by TCs from the ocean evaporation is brought about by the storm itself 22 , so TCs can induce anomalous moisture fluxes towards their locations. However, few studies [e.g. refs. 28 , 29 , 30 , 31 ] have investigated anomalies in moisture transport by TCs, which are necessary to understand the role of TCs in regional and global hydrological cycles. Extreme precipitation and moisture flux are positively correlated 3 . On this basis, the spatial pattern of anomalous moisture convergence tends to agree with the regions of anomalous cyclonic circulation in the tropical northeastern Pacific Ocean 28 . In particular, a climatological analysis of vertically integrated water vapour transport from hurricanes Matthew (2016) and Florence (2018) revealed the critical role of anomalous moisture flow from the Atlantic Ocean in producing extreme rainfall in North and South Carolina 30 .

Based on these previous studies and large-scale water and energy budgets, it remains unclear what happens with atmospheric moisture before it arrives at TC locations, which areas experience more evaporation than precipitation during TCs and vice versa, and whether the long-term tendencies of these anomalous moisture fluxes are linked to global warming. Therefore, this knowledge gap and the projected increase in atmospheric water vapour content at a rate of ~6–7% per degree of sea surface warming (on the basis of the Clausius–Clapeyron equation) 32 , 33 , 34 warrant further examination of the anomalous moisture fluxes induced by TCs and their impacts on regional and global hydrological cycles.

The extensive application of Lagrangian moisture tracking models 35 , 36 to investigate moisture source-sink relationships has been adopted in the last decade for examining anomalous moisture transport 37 , 38 and TCs 15 , 16 , 17 , 27 . Here, we performed a global analysis to identify areas where TCs induced anomalous surface freshwater flux (TC-induced evaporation minus TC-induced precipitation) between 1980 and 2018. We focused on quantifying the anomalies in moisture fluxes induced by TCs at the regional and global scales by applying a Lagrangian tracking method using global outputs from the FLEXible PARTicle dispersion model (FLEXPART) 39 . More details on the FLEXPART simulations and moisture tracking approach are available in the ‘Methods’ section.

Anomalous surface freshwater flux

TCs require substantial moisture availability to form and intensify 12 , 13 and consequently produce precipitation along their trajectories 27 , 40 , 41 , 42 . On this basis, TCs should induce anomalous moisture fluxes. Therefore, we examined the anomalies in TC-induced evaporation-precipitation patterns by applying a Lagrangian moisture tracking approach 43 , 44 to the pathways of atmospheric parcels within the outer radius of TCs from the global outputs of the FLEXPART model 39 fed by the ERA-Interim reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) 45 . For each TC position, we first computed the TC-related surface freshwater flux ( E - P ) pattern (see the ‘Methods’ section) and then estimated the annual surface freshwater flux and the corresponding anomalies over the study period for the same TC dates (Fig. 1 ).

a Surface freshwater flux (in mm) linked to tropical cyclones (TCs). b Climatological surface freshwater fluxes for the same dates of TCs. Greenish shade represents areas where the evaporation exceeds precipitation (positive surface freshwater flux) and bluish shade areas where the precipitation is higher than the evaporation (negative surface freshwater flux). c Significative surface freshwater flux anomalies (computing ( a ) − ( b )) during TCs. The level of significance at 95% was calculated using the Wilcoxon signed-rank test. d Surface freshwater fluxes anomalies in (%) during TCs. The percentage was computed as the difference between the TC-related surface freshwater flux and the climatological value, divided by the latter. The contour lines are schematic representations of areas of positive and negative surface freshwater flux anomalies and the corresponding change during TCs, and the arrows illustrate the changes in the surface freshwater flux during TCs relative to the climatological value. Supplementary Table 1 shows a comprehensive description of all annotations shown in panel (d). The analysis was performed using the FLEXible PARTicle dispersion model (FLEXPART) outputs fed by the European Centre for Medium-Range Weather Forecast ERA-Interim reanalysis from 1980 to 2018.

Contrasting the spatial distribution of the annual surface freshwater flux linked to TCs (Fig. 1a ) and the climatological pattern for the same TC time step during the study period (Fig. 1b ), TCs generally induced more evaporation and precipitation than the climatological values in the same areas (also in agreement with the annual surface freshwater flux field, Supplementary Fig. 1 ). As expected, during TC days, the widespread increase in evaporation (~>50%, Fig. 1d ) implies anomalous positive surface freshwater flux areas larger than negative ones over the tropics because the increased precipitation associated with TCs is more regionally concentrated (Fig. 1c ). The outstanding value of the large maximum of positive freshwater flux anomalies resulted from the relevance of TCs to moisture transport. These highly evaporative regions agree with previous findings that focused on the origin of moisture for the precipitation of TCs 15 , 16 , 17 . Meanwhile, the pattern of sink regions for TC days coincided with climatological areas of higher precipitation than evaporation (Fig. 1a and Supplementary Fig. 1 ), namely the Intertropical Convergence Zone (ITCZ) in both hemispheres 46 , the Western North Pacific Monsoon trough region, and southeastern Asia 47 . The nuclei of change (~50–80%, Fig. 1d ) in the negative surface freshwater flux anomalies are located in the western North Pacific Ocean (WNP) and northeast Pacific Ocean (NEPAC), which are intrinsically related to the higher TC frequency in these basins, accounting for ~31% 48 and ~20% 49 of the annual global TCs, respectively. Interestingly, based on a simple inspection of Fig. 1c, d , positive surface freshwater flux anomalies over southern Asia indicate a reduction in precipitation over this region during TCs by comparing analogous dates during the study period. Overall, the spatial pattern of the TC-related surface freshwater flux (Fig. 1 and Supplemenyary Fig. 2 ) revealed that the impact of TCs on the hydrological cycle is more significant in the Northern Hemisphere than in the Southern Hemisphere, which is likely related to the substantially larger number of TCs formed in the Northern Hemisphere (Supplementary Figs. 3 and 4 ).

Due to the impact of El Niño-Southern Oscillation (ENSO) on the basin-scale TC frequency, we also examined the possible influence of ENSO on the anomalous moisture transport induced by TCs from 1980 to 2018 (Fig. 2 ). The negative surface freshwater flux (precipitation>evaporation) induced by TCs was noticeably higher in absolute value during El Niño years than during La Niña years over southern Asia and along the average position of the ITCZ in the Pacific and eastern Indian Oceans, and lower over the Gulf of Mexico, southern United States and the Atlantic and western Indian Oceans ITCZ region during TC season (dark blue shaded area in Fig. 2d ). Similarly, the positive surface freshwater flux during El Niño exceeded that during La Niña over large sectors of both the northern and southern Pacific Ocean, Wharton and Perth basins, Arabian Sea, southern Bay of Bengal, Caribbean Sea, and western North Atlantic (dark green areas in Fig. 2d ). Meanwhile, it decreased in the Atlantic Ocean north and south of the ITCZ, central Indian Ocean south of the ITCZ, and Somali Basin in the western Indian Ocean (lime-green areas in Fig. 2d ).

Surface freshwater flux (in mm), linked to tropical cyclones during a warm (El Niño) and b cold (La Niña) phases of ENSO. Greenish-shaded regions represent areas where evaporation exceeds precipitation (positive surface freshwater flux), whereas bluish regions indicate areas where precipitation is higher than evaporation (negative surface freshwater flux). c Significant differences in surface freshwater flux during El Niño and La Niña years (computing ( a )−( b )). Significance was calculated at the 95% level using the Wilcoxon signed-rank test. d Schematic representation of areas of positive and negative surface freshwater flux differences (from ( c )) and the corresponding change during El Niño years; arrows illustrate the changes in the surface freshwater flux during El Niño years relative to those during La Niña years. Supplementary Table 1 shows a comprehensive description of all annotations shown in ( d ). The analysis was performed using FLEXible PARTicle dispersion model (FLEXPART) outputs fed by the European Centre for Medium-Range Weather Forecast ERA-Interim reanalysis from 1980 to 2018.

Surface freshwater flux trend

TCs have marked year-to-year variability at basin and global scales (Supplementary Fig. 4 ). Therefore, we also examined the annual trend in TC-related surface freshwater flux values from 1980 to 2018 (Fig. 3 ). By matching areas with statistically significant trends ( p  < 0.05) in the TC-related surface freshwater flux shown in Fig. 3 with the moisture source and sinks regions displayed in Fig. 1a , we detected an overall reduction of the TC-related surface freshwater flux across the globe, especially in the South Indian and Pacific Ocean basins. The sink regions over the eastern tropical Pacific Ocean and the Philippine Sea exhibited the highest decrease, dropping by up to 70–90 and 40–60 mm year −1 , respectively. Meanwhile, the surface freshwater flux over source regions has similarly decreased over the last four decades, exhibiting the highest drop over the Wharton and Perth basins in the South Indian Ocean at a rate of ~40–90 mm year −1 .

Filled areas show statistically significant surface freshwater flux tendencies (95% level of significance) between 1980 and 2018. A positive trend indicates a reduction in the surface freshwater flux over sink regions and negative values represent a decreasing trend in the surface freshwater flux over source regions. TCs sink and source regions are clearly identified in Fig. 1a .

The sensitivity of TC-related surface freshwater flux to sea surface temperature (SST) is also investigated. The annual mean SST was computed from the Daily Optimum Interpolation Sea Surface Temperature dataset 50 within the region of higher TC activity in each basin (see ‘Methods’ and Supplementary Fig. 5 ) for the dates of TC occurrence. By matching the spatial patterns of SST trends and the Spearman correlation coefficients between SST- and TC-related surface freshwater flux with the spatial distribution of TC-related surface freshwater flux trends (Fig. 3 ), two features stand out: SST exhibits a basin-wide statistically significant increasing trends and inversely correlates with the TC-related surface freshwater flux. It is worth noting that positive correlation coefficients coincide with sink regions (Fig. 1a ), and thereby, TC-related surface freshwater flux decreases with increasing SST. The former feature provides evidence of global warming, reaching noticeable values of 0.035–0.04 °C year −1 in NEPAC and South Indian Ocean (SIO) basins (Supplementary Fig. 6a ), and the second reveals a closer relationship between SST and TC-related surface freshwater flux. The sign of Spearman correlation coefficients coincides with that from TC-related surface freshwater flux trends (Supplementary Fig. 6b ). Overall, an increase in SST leads to a decrease in TC-related surface freshwater flux, which suggests that TC-induced precipitation is evolving to become similar to TC-induced evaporation. To examine this hypothesis, we computed the differences between the annual time series of basin and global scales lifetime accumulated evaporation and precipitation within the TC outer radius from the ERA-Interim reanalysis and Multi-Source Weighted-Ensemble Precipitation version 2 database (MSWEP V2) 51 . While the lifetime accumulated TC-induced evaporation minus precipitation within the cyclone outer radius (Supplementary Fig. 7 ) significantly increases in North Atlantic ocean (NATL) in agreement with Hallam et al. 52 , it exhibits an overall decrease in the remaining basins and globally, being statistically significant for the South Pacific and WNP basins and at global scale. Although the lifetime accumulated precipitation (evaporation) is highly dependent on TC frequency (Supplementary Fig. 4 ) and duration (Supplementary Fig. 8 ), it is an important metric for the influence of TCs on global water budgets 53 . While TC lifetime significantly increases in NATL, it decreases in WNP. Globally we did not detect any statistically significant trend in TC lifetime, thus TC activity in WNP seems to have a modulating role on the influence of TCs on the global hydrological cycle. In fact, WNP achieves ~30% and ~35% of annual global TC frequency and 6-hourly track points, respectively. The global trends of the differences between lifetime accumulated TC-induced evaporation and precipitation within the outer radius highlight the marked influence of TC frequency in the TC-related freshwater flux, as discussed above.

The overall decreasing trend in the TC-related surface freshwater flux is also found by computing its annual value. During the last four decades we found decreasing trends for all basins, except the NEPAC (Supplementary Fig. 9 ), globally a statistically significant trend of −40 mm year −1 is found (Fig. 4 ). We have concluded that for a 1 °C warming of SST, the TC-related surface freshwater flux reduces by 86% compared to its 1980 value. On average, global mean SST has increased by ~0.02 °C year −1 since 1980 (Supplementary Fig. 10a ) and is inversely correlated with the TC-related surface freshwater flux ( r  = −0.65). Similarly, average TC size increases by 6 km with 1 °C of SST warming and exhibits a statistically significant growth in the last four decades of 0.84 km year −1 (Supplementary Fig. 10b ). Previously, Pérez-Alarcón et al. 17 found a positive correlation between TC size and moisture uptake for TC precipitation. In line with this, the TC-related surface freshwater flux decreases with increasing TC outer radius by 8 mm year −1 ( p  < 0.1), indicating an overall increase in TC-related precipitation. Similarly, previous studies 15 , 16 , 17 pointed out that strong TCs tend to gain more moisture for generating the associated precipitation than weak TCs. In fact, a stronger TC usually produces a higher rain rate 54 . Additionally, although we found a non-statistically significant relationship between TC-related surface freshwater flux and the accumulated cyclone energy (ACE) during the study period, it was statistically significant (1.18 mm day −1  10 -4  kt −2 , p  < 0.05) after 1990, when global ACE significantly decreased (Supplementary Fig. 10d ), supporting the global reduction in the TC-related surface freshwater flux. Overall, the annual average of the Oceanic Niño Index and the interannual variability of SST, frequency, size and lifetime accumulated TC-induced evaporation and precipitation explain ~80.5% of the annual variability of TC-related surface freshwater flux.

The green line denotes the annual global TC-related surface freshwater flux (SFWF) and the dashed red line shows its statistically significant ( p <0.05) decreasing linear trend. The analysis was performed using FLEXible PARTicle dispersion model (FLEXPART) outputs fed by the European Centre for Medium-Range Weather Forecast ERA-Interim reanalysis from 1980 to 2018.

This study investigated and quantified anomalous moisture fluxes during TCs globally and, therefore, their impacts on the global hydrological cycle. By applying a Lagrangian moisture tracking method 43 , 44 to many atmospheric parcel trajectories from the outputs of the Lagrangian FLEXPART model 39 , we computed the anomalies in the surface freshwater flux during the TCs from 1980 to 2018 and the grid-to-grid trend in the surface freshwater flux budget. Although the Lagrangian approach neglects liquid water and ice content in the atmosphere, the mixing of air parcels, and the evaporation of precipitating hydrometeors 43 , 44 , this work represents the current state-of-the-art on the impacts of TCs on the global hydrological cycle.

The regions with positive anomalous surface freshwater fluxes induced by TC (Fig. 1 ) mostly agreed with the main moisture sources for the precipitation associated with them for each major ocean basin 15 , 16 , 17 . It is worth noting that Pérez-Alarcón et al. 15 , 16 , 17 identified some regions (e.g. Philippine Sea, South China Sea and eastern tropical Pacific Ocean) as moisture sources for the precipitation within the TCs outer radius that we identified here as sink regions. That means the evaporation from these regions contributes to the precipitation produced by TCs; however, in terms of the water budget, the TC-induced evaporation in these areas is lesser than the induced precipitation. Overall, TCs induced higher evaporation than the climatological value for analogous dates of TC occurrence and similarly induced precipitation above climatological values over the sink regions (negative surface freshwater flux). Despite the role of TCs in the annual rainfall amounts over Southern Asia 6 , we detected a statistically significant ( p  < 0.05) decrease in the absolute value of the negative surface freshwater flux in these regions during TCs on the same dates over the study period. This contradictory result underlines the fact that most TCs occur during the summer and autumn monsoon seasons, two periods with particularly high values of moisture transport from the ocean (and associated precipitation over land), independent of TCs occurrence. Previously, Chen et al. 55 noted that TC activity and seasonal monsoon climate may contribute in opposite manners to total rainfall in southern Asia, leading to complex interannual variability. Most recently, Chen et al. 56 detected that the early onset of the South China Sea summer monsoon after the 1990s favoured positive heating anomalies over South China, strengthening the East Asia Summer Monsoon and monsoon-related precipitation.

The spatial distribution of TC-related surface freshwater flux (Fig. 1 and Supplementary Fig. 2 ) reveals that TC frequency controls the impact of TCs on the global hydrological cycle. Large-scale and regional thermodynamic and dynamic conditions govern TC activity [e.g. ref. 57 ] and thus influence TC-related moisture transport patterns. The highest impact of TCs on the global water budget detected during the peak Northern Hemisphere TC activity in August and September (Supplementary Fig. 2 ) is probably closely related to the highest SSTs, which play a critical role in regulating global atmospheric circulation 58 , and the maximum northward equatorial position of tropical rain belts 59 . Although Sobel et al. 60 emphasised that global heat or moisture budgets are not useful for explaining the global number of TCs, seasonal TC frequency largely controls TC-related contributions to the global hydrological cycle.

The overall influence of ENSO on the annual TC-related surface freshwater flux, as shown in Fig. 2 , is linked to the impact of ENSO on TC frequency in each basin. In the Pacific Ocean, TC activity was enhanced during El Niño years 61 , 62 , whereas the NATL and Australian regions exhibited an overall reduction in the number of TCs 60 , 62 . The increase in anomalous moisture transport in the Pacific Ocean during El Niño events can also be attributed to convective anomalies over the western and central equatorial Pacific 63 . Conversely, the reduction in surface freshwater flux in the NATL basin is a response to the decrease in TC activity due to eastward displaced deep convection in the tropical Pacific, which enhances wind shear in the Atlantic Ocean 62 , the reduction in moist convection 64 and the weakening of northeasterly trade in the tropical Atlantic Ocean 65 . Nonetheless, the warm phase of ENSO intensifies the Caribbean Low-Level jet 66 , 67 , explaining the intensification of the TC-related surface freshwater flux over the Caribbean Sea in the NATL basin (Fig. 2 ). El Niño also enhances the negative surface freshwater flux over South Asia owing to the abnormal integrated vapour transport associated with westerly winds from the Indian Ocean and northern to southern China 68 . It also correlates negatively with TC-induced precipitation in the southern US 69 , which explains the reduction in anomalous moisture flux in this region. The surface freshwater flux differences in the Indian Ocean between the warm and cold phases of ENSO were noticeably weaker than those in the remaining basins. El Niño slightly increased the moisture supply from the Arabian Sea and southern Bay of Bengal (Fig. 2 ); however, this response was not significant in terms of changes in TC frequency 62 . TCs activity over the SIO was also substantially influenced by ENSO, which enhanced (suppressed) the number of TCs west of 75°E during El Niño (La Niña) years and exhibited an opposite tendency east of 75°E 60 , 62 . The surface freshwater flux differences in the SIO exhibited eastward intensification and westward weakening (Fig. 2c , d ), in line with the moisture source and transport patterns associated with TCs in that region 16 . Overall, the changes in large-scale mechanisms induced by ENSO influence the TC-related surface freshwater flux patterns in each basin by controlling the annual TC count. Nonetheless, more in-depth regional studies are needed to further investigate the modulatory role of ENSO in the anomalous surface freshwater flux in each basin.

Meanwhile, the overall statistically significant decreasing trend in the TC-related surface freshwater flux (Fig. 3 ) indicates that TC-induced precipitation has increased over the source areas or evaporation has increased over the sink regions, as revealed in Supplementary Fig. 9 . Chauvin et al. 26 pointed out that moisture support from evaporation decreases with increasing TC-rainfall intensities; however, this behaviour is not captured when evaluating the lifetime accumulated TC-induced evaporation and precipitation (Supplementary Fig. 7 ). Additionally, our results suggest that the reduction in the TC-related surface freshwater flux also results from a slight decrease in TC frequency (Supplementary Fig. 4 ) and lifetime (Supplementary Fig. 8 ) in the last two decades and to a significant rise in SST (Supplementary Fig. 10a ) over the study period. If this relationship remains invariable, we can hypothesise that in a warmer climate the global TC-induced precipitation will be higher than the TC-induced evaporation. Therefore, the increasing low-level moisture availability at a rate of 6–7% per degree of SST rising using the Clausius–Clapeyron relationship 32 , 33 , 34 in response to increasing evaporation due to global warming 70 can compensate for the reduction in TC-induced evaporation in regions far from the TC circulation and support the additional moisture required for excess TC-induced precipitation. Previous studies [e.g. ref. 71 ] have detected tropical precipitation change rates higher than that predicted from the Clausius–Clapeyron relationship. They argued that these super Clausius–Clapeyron rates of changes (rates of change larger than those predicted according to the Clausius–Clapeyron relationship) are linked to enhanced TC dynamics, which will compensate for latent heat release from TC precipitation, favouring rainfall intensity itself. As previously noted, surface evaporation from the TC underlying surface is lesser than TC-induced precipitation within the outer radius (Supplementary Fig. 9 ), confirming the role of the moisture flux convergence in supporting moisture for TCs from external sources 26 , 27 , 72 . The contribution of moisture flux convergence in the water budget is additive and should lead to supper Clausius–Clapeyron rates of changes 25 , 26 , 73 . This previous relationship supports the rate of changes of the TC-induced surface freshwater flux with increasing SST, which projects that in a warming world the TC-induced precipitation, including those that occurred outer of TC circulation during moisture transport towards TC locations, will be higher than the total TC-induced evaporation. Therefore, the water vapour deficit should be supplied by the increasing low-level moisture availability with SST rising. However, care must be taken in interpreting the decreasing TC-related surface freshwater flux with rising SST, firstly because the rate of changes were computed based on the annual value in 1980, and second, because the role of TCs in future changes in the hydrological cycle is not entirely clear within the scope of an inevitably warming world. We also acknowledge that a comprehensive assessment of the relationship between TCs and SSTs is rather more complex, as it must include other components not considered in our analysis. TC-related surface freshwater flux can modify the upper-ocean responses through its effects on sea surface salinity, causing sea surface freshening and enhancing stratification, which reduces oceanic mixing and SST cooling, resulting in a positive feedback to TC intensification [e.g. refs. 74 , 75 ]. Meanwhile, TC-induced surface wind stress has the opposite effect, leading to SST cooling, resulting in a negative feedback on the TC intensity through its impact on air-sea enthalpy fluxes [e.g. refs. 76 , 77 , 78 , 79 , 80 ]. Overall, the feedback from TCs to SST is controlled by a balance between the momentum flux of the storm and turbulent mixing in the upper ocean, and their relative magnitudes [e.g. ref. 75 ]. This relationship should be further investigated under global warming to understand the full impact of TCs on the global water cycle under climate change.

Additionally, based on the robustness of future reductions in the global frequency of TCs in response to global warming 81 , 82 , a reduction in surface freshwater flux induced by TCs is expected in the future. However, the results of Pérez-Alarcón et al. 15 , 16 , 17 revealed that intense TCs tend to gain more moisture to produce precipitation and therefore induce stronger moisture transport than weak TCs. Additionally, several studies [e.g. refs. 81 , 83 ] have indicated an increase in the number of intense TCs. Based on these previous findings, moisture transport related to the growth of intense TCs under global warming can compensate for the reduction in surface freshwater flux caused by the projected decrease in global TC count. Given the profound impact of global warming on TC frequency and intensity 81 , 82 , 84 , low-level moisture availability, column-integrated moisture 32 , 33 , 34 , TC-related rainfall 85 and atmospheric circulation patterns 86 , it is necessary to investigate the role of TC in hydrological cycles under climate change. This topic will be addressed in future studies.

In summary, the role of TCs in the hydrological cycle is strongly modulated by seasonal TC activity. This study confirms that TCs globally induce more evaporation and precipitation from moisture source and sink regions, respectively. Nonetheless, we provided evidence of a statistically significant globally decrease in TC-related surface freshwater flux, which could be attributed to a slight reduction of global TC frequency and lifetime in the last two decades. Additionally, we detected that TC-induced surface freshwater flux decreases approximately 86% per °C of SST warming. However, it should be noted that this rate of changes was estimated by comparing with TC-related surface freshwater flux in 1980. Overall, this study quantified the impact of TCs on global water budgets.

The observed TC trajectories for the North Atlantic and NE Pacific were obtained from the HURDAT2 dataset 87 provided by the US National Hurricane Center. The US Joint Typhoon Warning Center provided information on TCs in the remaining basins. These datasets contain the records at 6-h intervals (synoptic times), although HURDAT2 includes entries at non-synoptic times to indicate intensity maxima or landfalling. There have been approximately 100 years of TC records in some basins [e.g. ref. 87 ]; however, historical TC data have had the highest quality for climatological analysis since the beginning of satellite observations in the 1980s 88 . The TCSize dataset 89 provided the outer radii of all the TCs within the study period.

FLEXPART model simulations

The FLEXible PARTicle dispersion model (FLEXPART) model 39 is a Lagrangian model for tracking atmospheric moisture along air parcel trajectories. It was fed by the 6-h European Centre for Medium-Range Weather Forecast ERA-Interim reanalysis 45 with 61 vertical levels and 1° × 1° grid spacing in latitude and longitude. ERA-Interim data were available from 1979 to August 2019. Therefore, the study period was from 1980 to 2018.

Despite the coarse grid spacing of ERA-Interim data, the FLEXPART model forced with this reanalysis has been widely used to investigate the source-sinks relationship associated with different weather systems, i.e., atmospheric rivers 90 , extratropical 91 , subtropical 92 , and tropical cyclones 15 , 16 , 17 , 27 . Likewise, Fernández-Alvarez et al. 93 recently detected no significant differences between source-sink patterns from FLEXPART outputs forced with ERA-Interim and ERA5 reanalysis. Additionally, we acknowledge that several authors [e.g. refs. 26 , 94 ] have addressed the inability of ERA-Interim to capture extreme precipitation associated with TCs. Nonetheless, Pérez-Alarcón et al. 27 highlighted that TC-related precipitation estimated from the Lagrangian approach, using ERA-Interim as input data for the FLEXPART model, fits well with observations.

On this basis, the model version and simulation design were the same as those used by Pérez-Alarcón et al. 15 , 16 , 17 to identify the moisture sources for precipitation produced by TCs during their three well-known stages of development (genesis, lifetime maximum intensity, and dissipation). FLEXPART requires three-dimensional (temperature and specific humidity, horizontal and vertical wind components) and two-dimensional (total cloud cover, 10 m horizontal wind components, surface pressure, large-scale and convective precipitation, east/west and north/south surface stress, 2 m temperature and dew point temperature, topography, land-sea mask and subgrid standard deviation of topography, and sensible heat flux) fields as input data. To account for subgrid convective transport and turbulence in the planetary boundary layer, FLEXPART uses the convection parameterisation scheme proposed by Emanuel and Živković-Rothman 95 and solves the Langevin equations for Gaussian turbulence 96 , respectively.

During the simulations, FLEXPART divided the atmosphere into approximately two million air parcels of equal mass that were uniformly distributed, and the three-dimensional wind field advected air parcels throughout the atmosphere. We obtained the model outputs at a 6-h time step containing the information of each parcel, that is, position in latitude, longitude, and specific humidity.

Quantification of the anomalous moisture uptake

To estimate the water budget linked to the TCs, we backtracked the air parcels residing within the area delimited by the outer radius of the TCs at each system location for up to 10 days (240 h), which is considered the average time spent by moisture in the atmosphere from evaporation to precipitation 97 . Along the parcel pathways, the changes in specific humidity ( q , expressed in g kg −1 ) with time (d t  = 6 h) respond to moisture increases and decreases by evaporation ( e ) and precipitation ( p ), respectively, as illustrated by the Lagrangian water budget equation 43 , 44 (Eq. 1 ).

where m is the parcel mass (expressed in kg) and the left term ( e-p ) represents the freshwater flux of the parcel. By solving Eq. ( 1 ) for all parcels and amassing ( e-p ) over all N parcels residing in the atmospheric column over an area A (1° × 1°), we estimated the surface freshwater flux ( E - P ) as follows:

Backward analysis revealed the origin of the moisture in air masses during TCs events. That is, TCs induce net evaporation when ( E-P  > 0) and net precipitation when ( E-P  < 0). Therefore, regions in which evaporation (precipitation) exceeds precipitation (evaporation) can be considered moisture sources (sinks).

For each TC position, the climatological surface freshwater flux (including TC’s surface freshwater flux) was computed by averaging the surface freshwater flux for the same location and time step (month, day, and hour) of the TC over the study period. Thus, we verified whether the predominant evaporation (precipitation) areas during the TCs differed from the climatology by estimating surface freshwater flux anomalies. The surface freshwater flux anomalies for the TC time were obtained as the difference between the surface freshwater flux associated with the TC and the climatological flux.

TC-induced precipitation and evaporation within the outer radius

To account for the lifetime accumulated TC-induced precipitation (evaporation), we summed over the TC lifetime the total precipitation (evaporation) within a circle centred on the moving TC. All precipitation (evaporation) events within the TC outer radius are attributable to TCs. Previously, Lavender and McBride 53 and Hallam et al. 52 applied a similar approach to compute TC-related precipitation totals. TC-induced precipitation was extracted from the Multi-Source Weighted-Ensemble Precipitation V2 (MSWEP) database 51 . The MSWEP has high spatial (0.1° × 0.1° grid spacing) and temporal (3 h) resolutions and covers a long period (1979–present). It integrates observations from different data sources, e.g. surface rainfall gauge stations, satellite and reanalysis data. Additionally, it uses global streamflow observations at 13,762 stations for bias correction. Meanwhile, we computed TC-induced evaporation from the ERA-Interim reanalysis.

Annual mean sea surface temperature

As we are interested in the sea surface temperature (SST) during the TC occurrence, we used the US National Oceanic and Atmospheric Administration Daily Optimum Interpolation Sea Surface Temperature (OISST) dataset 50 to compute the annual mean SST at basin and global scale. This dataset merges observations from different platforms, such as satellites, ships and buoys, into a regular global grid of 0.25° × 0.25° in latitude and longitude. This dataset is available from September 1981 to the present. Thus, the analysis that included SST data was performed from 1982 to 2018. For each basin, we average the SST within the limited area by the highest TC activity (Supplementary Fig. 5 ) during TC occurrence, while for the global mean SST we average the SST of all the basins during TC dates.

Classification of ENSO years

To classify a TC season under El Niño La Niña, we followed the procedure of the US Climate Prediction Center based on the Ocean Niño Index (ONI, 3-month running mean of sea surface temperature anomalies in Niño 3.4 region). A year was classified as El Niño (La Niña) if the ONI remained higher (lower) than 0.5°C (−0.5 °C) for at least 5 consecutive months. If neither of these conditions was satisfied, the year was classified as an ENSO-neutral year. This approach was previously applied by Pérez-Alarcón et al. 97 and Colbert and Soden 98 to investigate the impact of the ENSO on the trajectories and moisture sources of the North Atlantic TCs formed in the main development region.

Data availability

The HURDAT2 database provided by the National Hurricane Center and the best track archives from the Joint Warning Typhoon Center are freely available at https://www.nhc.noaa.gov/data/#hurdat and https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks , respectively. The TCSize dataset can be freely downloaded from https://doi.org/10.17632/8997r89fbf .1. The ERA-Interim reanalysis supported by the European Centre for Medium-Range Weather Forecasts can be retrieved from https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ . MSWEP is available at http://www.gloh2o.org/mswep/ and the OISST database can be obtained from https://www.ncei.noaa.gov/products/optimum-interpolation-sst . The Ocean Niño Index (ONI) was obtained from the US National Oceanic and Atmospheric Administration - Physical Sciences Laboratory at https://psl.noaa.gov/data/climateindices/list/ . The Lagrangian moisture tracking method described in the ‘Methods’ section have been coded in Python in the TRansport Of water Vapor (TROVA) tool 99 , which is freely available at https://github.com/ElsevierSoftwareX/SOFTX-D-22-00100 . Meanwhile, the FLEXPART source code is available at https://www.flexpart.eu/downloads/6 .

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Acknowledgements

A.P.-A. thanks the support from the Xunta de Galicia (Galician Regional Government, Consellería de Cultura, Educación e Universidade) under the Postdoctoral grant No. ED481B-2023/016. J.C.F.-A. and P.C.-H. acknowledge support from the Xunta de Galicia (Consellería de Cultura, Educación e Universidade) under PhD grants No. ED481A2020/193 and ED481A2022/128, respectively. R.M.T. was supported by the Portuguese Science Foundation (FCT) through the project AMOTHEC (DRI/India/0098/2020). EPhysLab members are supported by SETESTRELO project (grant no. PID2021-122314OB-I00) funded by the Ministerio de Ciencia, Innovación y Universidades, Spain (MCIN/10.13039/501100011033) and Xunta de Galicia (grant ED431C2021/44; Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva), Consellería de Cultura, Educación e Universidade), and by “ERDF A way of making Europe”. This work has also been possible thanks to the computing resources and technical support provided by Centro de Supercomputación de Galicia (CESGA) and the Red Española de Supercomputación (RES).

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Home » Air » Meteorological phenomena » Tropical Cyclones: impacts and risks

Tropical Cyclones: impacts and risks

As the ruins of Tacloban in the Philippines show, after the passage of Typhoon Hayan in November 2013, cyclones, together with earthquakes, are the most devastating natural phenomena. Specialized meteorological services monitor them around the world to warn the populations concerned of the associated risks. But we still have to be well prepared for these terrible deadlines.

1. Impacts of tropical cyclones

2. tropical cyclone forecasting, 3. risk prevention.

Every year, cyclones, typhoons and hurricanes affect dozens of countries around the world. Losses of life and material damage are significant due to strong winds, heavy rains, large swells and storm surges. Hazardous phenomena are not only located on islands and coasts. Even mitigated, hurricanes often cause damages inland, through floods and landslides, sometimes hundreds of kilometres from the ocean.

Over the past 50 years, nearly one million people have died in tropical cyclones. The cause is the increase in populations in exposed areas, due to the attraction of the sun in rich countries, population growth elsewhere. In developed countries, the loss of human lives has decreased significantly as a result of improved forecasts. But at the same time, the average cost of devastation has risen sharply . The record is held by Hurricane Katrina , whose passage over New Orleans in August 2005 left a bill of at least €100 billion . In terms of human lives, the most affected regions were Bangladesh with the two hurricanes of November 1970 and April 1991, each claiming more than 200,000 lives, Honduras and Nicaragua with Mitch in October 1998, Burma in 2008 with Nargis ravaging the Irrawaddy Delta, the Philippines with Hayan in 2013, each causing tens of thousands of deaths.

Hurricane winds are among the most powerful on Earth. Below the eyewall, they often exceed 200 km/h and can reach 350 km/h. Only tornadoes produce stronger winds, but on smaller scales and for shorter periods of time. The distribution of winds is rarely symmetrical: they are often more intense on the right (resp. left ) of the cyclone with respect to its displacement in the northern (resp. southern ) hemisphere . When a cyclone passes, debris of all sizes carried by the wind becomes projectiles that hit everything exposed. However, it is not the sustained winds that cause the most damage, but rather variations in intensity and direction that weaken the structures. Variability increases inland as topography generates small-scale (a few kilometers) and locally more intense circulations.

Predicting the trajectory and evolution of cyclones is the main task of the CMRS. Thanks to improved observations and numerical models , the average statistical error has decreased by about 1% per year in recent decades. The uncertainty about the position of the centre of a cyclone, derived from satellite images, is a few tens of kilometres. With regard to forecasts, uncertainty is increasing by about 100 kilometres per day of the forecast period. The position of a cyclone at 24, 48 or 72 hours is therefore only expected within margins of about 100, 200 or 300 kilometres . These are only average values and the reliability of forecasts varies according to cyclones and their environment. In addition, a few large errors often have a much stronger impact on the public than the more correct forecasts. It is difficult to predict slower and more erratic trajectories. Progress in predicting the intensity of cyclones – wind strength, amount of precipitation , wave and tidal amplitude – is more limited due to the complexity of internal processes and interactions with the environment.

After a hurricane event, devastation requires rapid action and significant state support , which is only possible with some effectiveness in developed countries. Elsewhere, the scars left by a cyclone can last for years. The considerable damage that must be reimbursed can put insurance companies in difficult situations. Given the scale of the claims (hundreds of millions to tens of billions euros), these companies also reinsure themselves with other companies. Through a game of financial dominoes, the passage of a cyclone over a tropical country can trigger economic storms in the quieter markets of London, Zurich, New York or Tokyo.

References and notes

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The Encyclopedia of the Environment by the Association des Encyclopédies de l'Environnement et de l'Énergie ( www.a3e.fr ), contractually linked to the University of Grenoble Alpes and Grenoble INP, and sponsored by the French Academy of Sciences.

To cite this article: Frank ROUX (March 11, 2019), Tropical Cyclones: impacts and risks, Encyclopedia of the Environment, Accessed May 21, 2024 [online ISSN 2555-0950] url : https://www.encyclopedie-environnement.org/en/air-en/tropical-cyclones-impacts-and-risks/ .

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Following Emanuel's hypothesis about tropical cyclones, other people have investigated the impact of cyclones on OHT and the possibility of cyclones causing equable climates. The other studies have shown that tropical cyclones do increase OHT; however, there are some aspects of the system that Emanuel did not consider in his first paper.

Ryan L. Sriver and Matthew Huber examined SSTs before and after tropical cyclone events to see the effects of cyclones on OHT. They used the difference in temperatures to estimate the amount of vertical mixing that occurred as a result of cyclones, and they assumed that any cooling that occurred on the sea surface was a result of vertical mixing. After they had obtained estimates for the amount of vertical mixing, they assumed that all of the heat diffused downward eventually had to have been translated into OHT. Their results showed that roughly 15% of peak OHT is attributable to mixing induced by tropical cyclones and that about half of the thermohaline circulation may result from this same vertical mixing (Sriver and Huber, 2007). Additionally, they found that tropical SSTs are highly correlated to globally integrated annual ocean heat content (OHC) and OHT (r 2 =0.73). When tropical SSTs decrease, OHC and OHT increase. As a result, their data supports the idea that cyclone activity increases OHT out of the Tropics, but they fail to acknowledge one important aspect of OHT.

Sriver's and Huber's study shows a correlation between cyclone activity, SST, and OHT. (Sriver and Huber, 2007)

The ocean is very good at removing heat from the Tropics and moving it to the mid-latitudes; however, by about 50° latitude, the ocean does not transport much heat further north. Instead, the atmosphere handles most of the heat transport after that point. This fact hurts Emanuel's hypothesis because the ocean does not to bring heat to the high-latitudes, so even if tropical cyclones were to increase in frequency or intensity, the heat would only be transported to the mid-latitudes. Therefore, it seems unlikely that tropical cyclones would be fully responsible for equable climates.

Ocean OHT (green) drops off by about 50°. (MIT's CMI)

Work by Malte Jansen and Raffaele Ferrari also has exposed a problem with Emanuel's idea. In their paper from 2009, Jansen and Ferrari examine the effects on OHT based on where mixing occurs. They argue that cyclones do not mix the entire area around the equator but rather that they generally mix certain latitude bands. They base this idea off of the fact that tropical cyclones rarely tend to happen equatorward of 8° to 10° latitude (Jansen Ferrari, 2009). This trend exists because water slightly north and south of the equator actually tends to be warmer than water at the equator because upwelling of cold, deep water at the equator cools the sea surface there.

Cyclones tend to occur in bands off of the equator. (Jansen and Ferrari, 2009)

Using this fact as a basis, Jansen and Ferrari performed a study to examine the effects of mixing when it occurs in different latitude bands. In the study, they performed four different runs: a control run that simulated normal conditions, a "no gap" run that had mixing between 31°N and 31°S, a run with mixing between 5.6° and 31° latitude, and a run with mixing between 11.2° and 31° latitude. The "no gap" run revealed that when mixing occurs throughout all of the low-latitudes poleward OHT increases. The runs with gaps, however, showed a different result. When mixing did not occur in a gap around the equator, both poleward and equatorward OHT existed. The model demonstrated that two different circulation cells could form with water moving away from the mixing band and sinking in the poles and at the equator. Because some of the heat would move toward the equator, having a gap would reduce poleward OHT. In their own words, "mixing triggered by [tropical cyclones] primarily induces an equatorward transport of heat and results in an overall decrease of poleward OHT out of the equatorial region" (Jansen and Ferrari, 2009). Therefore, their study contradicts Emanuel's thoughts. While it is possible that tropical cyclones could increase poleward OHT, they are more likely to reduce poleward OHT and to increase equatorward OHT because they rarely occur directly over the equator. Using the characteristics of tropical cyclones' locations as their base, Jansen and Ferrari show that it is unlikely that tropical cyclones instigated equable climates.

The "no gap" run increases poleward OHT, while the gap runs show both poleward and equatorward OHT. (Jansen and Ferrari, 2009)

Website Written and Designed by Mark E. Piana

• 1. Introduction
• a. Beta drift
• b. Computational method for TC tracks
• 3. Verification by TC occurrence frequency
• 4. Verification by prevailing TC tracks
• 5. Verification by TC landfall
• 6. Concluding remarks

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Computational flowchart for a TC track.

TC occurrence frequency obtained from (a) the observed data, (b) the computed results, and (c) the difference between (a) and (b) with the proposed TC trajectory model in the western North Pacific Ocean basin during the period 1979–2018.

TC occurrence frequency obtained from (a) the observed data, (b) the computed results, and (c) the difference between (a) and (b) with the proposed TC trajectory model in the North Atlantic Ocean basin during the period 1979–2018.

The prevailing tracks of the three TC types averaged from the observed data (black lines) and the computed results with (a) Wu and Wang’s (2004) model, (b) Emanuel et al.’s (2006) model, and (c) the present model in the western North Pacific Ocean basin during the period 1979–2018. For the computational results, the green, red, and blue lines represent the SM, CL, and CO types, respectively.

The distributions of TC landfall number by latitude in the western North Pacific Ocean basin during the period 1979–2018 obtained from the observed data (solid lines) and computed results (dashed lines).

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A Simple Trajectory Model for Climatological Study of Tropical Cyclones

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The establishment of a tropical cyclone (TC) trajectory model that can represent the basic physics and is practically advantageous considering both accuracy and computational cost is essential to the climatological studies of various global TC activities. In this study, a simple deterministic model is proposed based on a newly developed semiempirical formula for the beta drift under known conditions of the environmental steering flow. To verify the proposed model, all historical TC tracks in the western North Pacific and the North Atlantic Ocean basins during the period 1979–2018 are simulated and statistically compared with the relevant results derived from observed data. The proposed model is shown to well capture the spatial distribution patterns of the TC occurrence frequency in the two ocean basins. Prevailing TC tracks as well as the latitudinal distribution of the landfall TC number in the western North Pacific Ocean basin are also shown to agree better with the results derived from observed data, as compared to the existing models that took different strategies to include the effect of the beta drift. It is then concluded that the present model is advantageous in terms of not only the accuracy but also the capacity to accommodate the varying climate. It is thus believed that the proposed TC trajectory model has the potential to be used for assessing possible impacts of climate change on tropical cyclone activities.

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Tropical cyclones (TCs), which may cause strong winds, torrential rainfall, and storm surges in coastal areas, are among the most devastating natural phenomena in the world ( Pielke et al. 2008 ; Park et al. 2014 ; Walsh et al. 2019 ). In recent years, a public concern is whether global warming has a major impact on TC activities, including the variability of TC occurrence frequency in a particular ocean basin, the migration of the geographical locations at which TCs experience their lifetime-maximum intensities, the future trend of landfall possibility along a particular coast, and so on. As a result, a significant number of studies have been carried out to investigate the climatological variability of TC tracks. In the literature, these studies generally fall into two categories: whether climate change to date has already had a detectable effect on TC tracks and how climate change might affect TC tracks in the future ( Yin 2005 ; Kossin et al. 2010 , 2016 ; Walsh et al. 2019 ).

Various methods have been developed to depict TC tracks. In general, these methods can be distinguished between mesoscale atmosphere–ocean coupled methods and global atmospheric circulation methods. The global atmospheric circulation methods can be further classified as having a direct approach, a deterministic approach, or a statistical approach. The mesoscale atmosphere–ocean coupled methods are theoretically rigorous ( Zhang and Emanuel 2016 ; Chen et al. 2018 ), but they cannot be applied to climatological studies at present time due to their critical requirement on computer resources. The direct approach with a global atmospheric circulation model is to identify TC-like vortices in the output of a general circulation model (GCM) ( Murakami and Wang 2010 ; Walsh et al. 2019 ). Previous studies based on the direct approach have yielded results with uncertain accuracies, indicating the deficiency of low-resolution GCMs in resolving actual TCs ( Murakami et al. 2011 ; Yokoi et al. 2013 ). More recently, Tory et al. (2013a , b , c) proposed a direct approach using the so-called Okubo–Weiss–Zeta method ( Provenzale 1999 ) for TC detection. This approach yields reasonable results from both fine- and coarse-resolution GCMs ( Chand et al. 2017 ; Bell et al. 2018 , 2019 ), but it is still reported to be problematic for TC tracks in coastal regions ( Tory et al. 2013b ). The statistical approach integrated into a GCM ( Emanuel et al. 2006 ; Hall and Jewson 2007 ) is totally or partially based on the statistical properties of TC parameters derived from historical observations ( Emanuel 2000 ; Yu et al. 2016 ). TC tracks obtained with this approach are synthetic and meaningful only from the statistical point of view. The most critical problem in the statistical approach is probably a possible lack of consistency for the statistical properties of parameters due to changing climate. The deterministic approach integrated into a GCM has significant advantages as compared to both the direct approach and the statistical approach, and is the major concern of this study.

The deterministic approach integrated into a GCM often requires a careful consideration of the fundamental physics for TC motion ( Wu and Wang 2004 ; Wu et al. 2005 ; Emanuel et al. 2006 ; Colbert et al. 2013 , 2015 ; Lin and Emanuel 2016 ). With this approach, TC tracks must then be jointly determined by the large-scale environmental steering flow and the beta drift ( Carr and Elsberry 1990 ; Wang et al. 1998 ; Chan 2005 ). The environmental steering flow plays a central role in driving the TC forward, while the beta drift represents a major deviation of the TC motion from the environmental steering flow. Compared with the statistical approach, TC tracks obtained with the deterministic approach may simulate actual events and have no problem in principle to include the effects of climate change ( Wu and Wang 2004 ; Wu et al. 2005 ; Colbert et al. 2013 , 2015 ; Lin and Emanuel 2016 ). Compared with the direct approach, the deterministic approach does not require to resolve the motion of vortices. The TC tracks obtained are then less dependent on the resolution of the GCM and, therefore, more reliable ( Lin and Emanuel 2016 ).

The environmental steering flow is defined as the large-scale motion of the atmosphere around the TC, averaged both vertically and horizontally ( Chan and Gray 1982 ), which may be conveniently obtained from the observed data ( Chan and Gray 1982 ; Chu et al. 2012 ; Colbert and Soden 2012 ). Formulation of the beta drift, on the other hand, does not seem to be easy ( Wu and Wang 2004 ; Emanuel et al. 2006 ; Zhao et al. 2009 ; Colbert and Soden 2012 ; Colbert et al. 2015 ). Wu and Wang (2004) adopted the mean beta drift velocity derived from the observed data. Emanuel et al. (2006) assumed the beta drift velocity to be a constant vector over a regional ocean. Note that Wu and Wang’s (2004) beta drift velocity cannot be simply given by a formula. Based on the study of Wu and Wang (2004) , Zhao et al. (2009) proposed an empirical relation between the mean beta drift and the large-scale environmental flow. Empirical relations between the magnitude of the beta drift velocity and the angle of the TC track were also suggested ( Colbert and Soden 2012 ; Colbert et al. 2015 ). Note that the physical mechanism behind the beta drift was ignored in all these studies.

Fiorino and Elsberry (1989) provided a comprehensive explanation on the beta drift. The underlying physics was then further clarified by Chan (2005) . According to these authors, the beta drift velocity depends not only on the size and intensity but also on the inner structure of the cyclone. Basically, it was realized that the nonlinear interaction between the cyclone and the meridional gradient of the Coriolis parameter is the primary mechanism for the beta drift. There are also studies indicating that the nonuniformity of the environmental flow certainly affects the beta drift ( Wu and Emanuel 1993 ; Williams and Chan 1994 ; Wang and Li 1995 ; Li and Wang 1996 ). The large-scale horizontal shear in the environmental flow seems to be a key factor in determining the TC motion in most cases ( Chan 2005 ). This point was further confirmed by a statistical study of the influence of large-scale environmental flows on the climatological characteristics of the beta drift ( Zhao et al. 2009 ). In addition, the kinetic energy transferred into the structured inner core of the cyclone was also identified to be proportional to the horizontal shear in the environmental flow ( Li and Wang 1996 ). There were also arguments about the effect of the vertical shear in the environmental flow ( Wu and Emanuel 1993 ), but it is certainly less important to the beta drift than the effect of the horizontal shear ( Chan 2005 ).

Existing models for TC tracks including the effect of the beta drift ( Wu and Wang 2004 ; Emanuel et al. 2006 ; Zhao et al. 2009 ; Colbert and Soden 2012 ; Colbert et al. 2015 ; Chen et al. 2018 ) are either too sophisticated to be practically used in climatological studies or not accurate enough. The main objective of the present study is to establish a climatologically effective TC trajectory model that is simple enough to be readily applicable in a GCM but can correctly include the effect of the beta drift given the large-scale environmental flow condition. A brief verification of the proposed model is also provided.

2. A simple TC trajectory model

According to Fiorino and Elsberry (1989) and Chan (2005) , the nonlinear interaction between a mesoscale TC circulation and the meridional gradient of the Coriolis parameter leads to a pair of counterrotating gyres (i.e., the beta gyres) within the core of the TC. The cyclonic gyre is located at the southwest and anticyclonic gyre at the northeast within a TC in the Northern Hemisphere. At the same time, there is a near-uniform ventilation flow between the two gyres, or across the TC center, which is thus directed to the northwest in the Northern Hemisphere. It has been demonstrated that this ventilation flow associated with the beta gyres somehow plays a dominant role in determining the beta drift velocity. It is then of special interest to find a general expression for this ventilation flow velocity so that a formula for the beta drift velocity can be obtained.

To describe the evolution of the beta gyres, we consider the conservation of kinetic energy within the gyres following Wang and Li (1995) :

where E represents the domain-integrated kinetic energy within the beta gyres while F is the net energy flux to the developing gyres; d / dt denotes the rate of change observed when following the advection of the beta gyres.

Based on a numerical study of Li and Wang (1996) , the rate of kinetic energy transferred to the beta gyres from the environmental flow is proportional to the horizontal shear of the environmental flow (∂ V /∂ x + ∂ U /∂ y ), that is,

where U and V denote the zonal ( x ) and meridional ( y ) components of the environmental flow, respectively; α is the azimuthal angle of the beta gyres, that is, the azimuthal angle of the anticyclonic gyre center measured counterclockwise from due north, and sin2 α < 0 when the anticyclonic gyre is located in the northeast quadrant in the Northern Hemisphere. Thus, a positive horizontal shear allows the environmental flow to feed kinetic energy to the beta gyres so that the beta drift is accelerated, and vice versa. As a support to Eq. (2) , it is known that the rate of the kinetic energy transferred to the beta gyres from the environmental flow is nearly independent on the TC intensity and structure ( Li and Wang 1996 ).

Substituting Eq. (2) into Eq. (1) and completing the integration under the assumption that the environmental flow is quasi-steady, we have

where E 0 is the kinetic energy possessed by the beta gyres at t = t 0 and a 0 is a dimensionless constant. By Eq. (3) it is possible to estimate the representative circumferential velocity of the beta gyres within a characteristic time interval τ :

where W is the representative circumferential velocity of the beta gyres; W 0 and a τ are constants to be determined. The ventilation flow associated with the beta gyres is expected to have a velocity with magnitude proportional to W . Including the effect of omitted factors, the beta drift velocity may then be expressed as

where u β is the beta drift velocity; u 1 is introduced to represent the omitted effect of those factors that cannot be represented by the horizontal shear in the environmental flow, such as the intensity and the inner structure of the TC as well as the vertical nonuniformity and the horizontal acceleration of the steering flow; and | u 2 | = W 0 .

To determine the constants in Eq. (5) we may have to rely on the statistical information on TCs in a particular ocean basin. Over the western North Pacific basin, the mean beta drift velocity—that is, the averaged vectorial difference between TC advection velocity and the large-scale steering flow velocity, based on the records of TC events observed from 1965 to 2007—has an orientation of 320° and a magnitude of 3.3 m s −1 according to Zhao et al. (2009) . Chen and Duan (2018) found that the mean beta drift velocity over the western North Pacific basin has an orientation of 310° and a magnitude of 2.9 m s −1 , based on the observed data for TCs during 1949–2014. In addition, a numerical study of Fiorino and Elsberry (1989) showed that the orientation of the ventilation flow between the beta gyres in the Northern Hemisphere may rotate by about 45° from initially northward to finally northwest, and the direction of the ventilation flow remains stationary once the beta gyres are eventually formed. These studies all suggest that the mean beta drift velocity possesses a directional angle of about 315° and a magnitude between 1.0 and 4.0 m s −1 , although some discrepancies do exist due to variations in the period of study. Therefore, we adopt the following expressions for the zonal and meridional velocity components of the beta drift over the western North Pacific basin:

where W β = 1.0 m s −1 and γ = 2000s. In Eqs. (6) and (7) , the magnitudes of u β and υ β are the same but their signs are different because previous studies suggested that the beta drift has a directional angle of about 315°. The value of γ is determined so that the magnitude of the beta drift velocity mostly falls into the range between 1.0 and 4.0 m s −1 as the observed horizontal shears in the environmental flows around all TCs occurred over the western North Pacific basin during the period 1979–2018 are substituted.

It is worthwhile to mention that a TC trajectory model with the beta drift velocity expressed by Eqs. (6) and (7) must not be expected to accurately depict the motion of an actual TC event. The model is applicable only in climatological studies of TC tracks. This is not only because the model is simple and many small-scale meteorological factors that affect the motion of a TC are omitted but also because the parameters in the model are statistically calibrated with observed data.

In climatological studies, a TC is often treated as a point vortex that moves from its genesis position. The track of the TC can then be determined by connecting the discretized positions of the moving TC center solved from the following difference equations:

where x and y are the zonal and meridional coordinates of the TC center, respectively; the subscript n indicates a value at t = t 0 + n Δ t while t 0 is the time when the TC is initially generated; Δ t is the time step and Δ t = 6 h is widely adopted in practical studies of TC tracks.

It is now clear that a TC track can be uniquely determined once the environmental steering flow velocity components U and V are available over the lifetime of the TC event. The environmental steering flow in this study is defined as the flow averaged both in the horizontal plane and in the vertical direction. Horizontally, it is averaged over a 5°–7.5° radial band from the TC center ( Chan and Gray 1982 ). Vertically, a deep-layer mean ( Holland 1984 ; Deng et al. 2010 ; Colbert and Soden 2012 ) is taken considering the wind field at levels of 850, 500, and 200 hPa. Historical information of the horizontal wind field can be obtained at a 2.5° × 2.5° resolution and a 6-h interval from the reanalysis data of the National Centers for Environmental Prediction and National Center for Atmosphere Research (NCEP–NCAR) ( Kalnay et al. 1996 ). It may be worthwhile to mention that, when applied to a climatological study of TC tracks, the model yields a slightly worse but still very good results when the monthly mean wind data are used in place of the instantaneous data.

The computational algorithm for obtaining a TC track can be summarized in Fig. 1 . The major steps include (i) setting the genesis position and time as the initial conditions of a computational step; (ii) reading the horizontal winds at the known time step and at levels of 850, 500, and 200 hPa from the reanalysis data; (iii) computing the environmental steering flow; (iv) computing the horizontal shear of the environmental flow; (v) computing the beta drift velocity; (vi) renewing the position of the TC center; and (vii) repeating (ii) to (vi) until the TC moves out of the domain of interest or up to 10 days.

Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0285.1

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To validate the proposed TC trajectory model as well as the formulation for the beta drift velocity, historical TC tracks in a prescribed period may be simulated to yield the TC occurrence frequency in an ocean basin of interest, which may then be compared with the results derived from observed data. The frequency of TC occurrence within a unit area is defined as the mean annual number of TCs generated within or passing through this area. The unit area is taken to be a 5° × 5° grid element in this study. Note that a given TC is counted only once in a particular unit area. To focus on model verification, our attention is especially paid to the TC tracks in the western North Pacific basin (i.e., the ocean basin bounded by 0°–50°N, 100°E–180°). The period of study is 40 years from 1979 to 2018, since the TC track data after 1979 are often considered to be more reliable due to availability of satellites for TC observations. Based on the records in International Best Tracks Archive for Climate Stewardship (IBTrACS) dataset v04 ( Knapp et al. 2010 ), there are totally 935 TC events observed in the western North Pacific basin within the period of our study. We compute the tracks for all 935 TC events with the present TC trajectory model. In the computations, the initial time and position of each TC event are specified according to the observed values recorded in IBTrACS dataset v04. The relevant environmental steering flow velocity is derived from the NCEP–NCAR dataset.

As shown in Fig. 2 , the computed pattern of the TC occurrence frequency in the western North Pacific Ocean basin is very close to that obtained from the observed data. The correlation coefficient of the two spatial patterns is actually 0.96. In particular, the computed and observed maxima of the TC occurrence frequency almost coincide at 20°N, 125°E. Both observed and computed results show that TCs occur most frequently over the Philippine Sea. The observed and computed high-frequency regions are nearly overlapped, in which the relative error of the local occurrence frequency is less than 10%.

The proposed TC trajectory model is established based on statistical facts about TC motion over the western North Pacific basin. It is thus not certain if the model is valid globally, even though the physics underlying the formulation is universal. To test the applicability of the model in other ocean basins, the TC occurrence frequency in the North Atlantic Ocean basin is also computed in this study. Based on the IBTrACS dataset v04, there are totally 428 TC events observed in the North Atlantic basin (i.e., the ocean basin bounded by 0°–50°N, 100°–10°W) within the period of our study. The tracks for all 428 TC events are then simulated with the present TC trajectory model. The empirical values for W β and γ in Eqs. (6) and (7) remain unchanged.

As shown in Fig. 3 , the computed pattern of the TC occurrence frequency in the North Atlantic basin is also very close to that obtained from observed data. The correlation coefficient of the two spatial patterns is 0.93. The computed and observed maxima of the TC occurrence frequency almost coincide at 35°N, 70°W. The observed and computed high-frequency regions of TC occurrence are highly similar, in which the relative error of the local occurrence frequency is less than 10%. It is thus clear that the performance of the proposed TC trajectory model in the North Atlantic basin is just as good as its performance in the western North Pacific basin, although the model parameters were calibrated only by statistical data for TCs over the western North Pacific basin. This may imply that the proposed TC trajectory model is generally effective.

To further validate the TC trajectory model proposed in this study, we try to compare the geometric features of the computed TC tracks with those obtained from observed data. As a reference, results given by existing models, including the mean beta drift model based on observed TC data proposed by Wu and Wang (2004) and the constant vector model proposed by Emanuel et al. (2006) , are also presented. To avoid reiteration, we consider only TC tracks in the western North Pacific Ocean basin (i.e., the ocean basin bounded by 5°–20°N, 135°E–180°). Special attention is paid to the prevailing tracks identified by the method of Colbert et al. (2015) . According to Colbert et al. (2015) , TC tracks in the western North Pacific Ocean basin can be classified into three different types: straight-moving (SM), curved to landfall (CL), and curved to the ocean (CO). Note that TCs of the same type often have similar active season, life cycle, intensity, and landfall impact ( Camargo et al. 2007 ). For example, the SM type may threaten the Philippine Islands and southern China; the CL type may make landfall along the eastern coast of China, or the coast of the Korean peninsula and Japan; and the CO type is usually less disastrous because most TCs of this type do not land. Based on the records in IBTrACS dataset v04, there are totally 494 TC events observed in the western North Pacific Ocean basin during the period 1979–2018 that satisfy the definitions of the aforementioned three types, of which 170 TCs belong to the SM type, 220 TCs to the CL type, and 104 TCs to the CO type.

We compute the tracks for all 494 TC events with the mean beta drift model proposed by Wu and Wang (2004) , the constant vector model proposed by Emanuel et al. (2006) and the present model, respectively. In the computations, the initial time and position of each TC event are specified following the observed data. The relevant environmental steering flow velocity is derived from the NCEP–NCAR dataset.

The computed TC tracks with each beta drift model are then reclassified into the three different types, respectively, according to the definition of Colbert et al. (2015) . Note that a TC track that belongs to one type based on the observed data may fall into another type when it is simulated by a particular trajectory model because the classification of marginal tracks may be changed when the numerical error is not negligible. Shown in Table 1 are the TC numbers of different types as different beta drift models are employed. It is seen that the results computed with the present model are much closer to the observed data. The number of CL-type TCs is evidently underestimated and the number of CO-type TCs correspondingly overestimated with Wu and Wang’s (2004) model, whereas the number of SM type is clearly underestimated and the number of CO type overestimated with Emanuel et al.’s (2006) model.

TC count by type. The values in parentheses are the differences between observed and computed results.

It is necessary to point out that the mean beta drift in Wu and Wang’s (2004) model is determined by the vectorial difference between a 40-yr mean of the TC advection velocity and the relevant environmental steering flow velocity over the entire western North Pacific Ocean basin. The direction of the mean beta drift velocity was found to be northwest in general. However, it is locally northeastward near the subtropical high in the middle latitudes. Note that the subtropical high is usually active for a long time in the Northern Hemisphere. The less emphasized consideration of the effect of the subtropical high in Wu and Wang’s (2004) model is probably the major reason for the computational error of the mean beta drift in the middle latitudes, which finally results in a change of some TC tracks of the CL type based on the observed data to the CO type. Other researchers ( Zhao et al. 2009 ) also pointed out this problem when using the mean beta drift given by Wu and Wang (2004) .

The beta drift velocity in Emanuel et al.’s (2006) model is assumed to be a constant vector directed to the north over the whole western North Pacific Ocean basin. Since the environmental steering flow is generally westward at the low latitudes and eastward at the middle latitudes, driven by the large-scale summer circulation in this ocean basin, omitting the westward component of the beta drift then leads to an underestimation of the westward component of the TC advection velocity in the low latitudes and an overestimation of the eastward component of the TC advection velocity in the middle latitudes with Emanuel et al.’s (2006) model, which finally causes a significantly reduced number of the westward TC tracks and excessive TC tracks being classified into the CO type.

The prevailing tracks of the three types averaged from the computational results obtained with different beta drift models as well as from the observed data are demonstrated in Fig. 4 . The prevailing tracks averaged from the results computed with the present beta drift model are shown to have the best agreement with the relevant ones from the observed data. The prevailing tracks of CL and CO types averaged from the results computed with Wu and Wang’s (2004) model are more eastward than the observed ones and the errors further increase at the middle latitudes. As mentioned above, this computational error is probably due to a less emphasized consideration of the effect of the subtropical high. It is also evident that the prevailing track of the SM type obtained with Wu and Wang’s (2004) model matches relatively well with the observed one but is still slightly deviated to the southwest. The prevailing tracks of CL and CO types averaged from the results computed with Emanuel et al.’s (2006) model are even more deviated to the east while the prevailing track of the SM type shows a curving trend instead of going straightforwardly to the west. The computational error is likely due to omission of the western component of the beta drift velocity in Emanuel et al.’s (2006) model.

It is certainly important for a trajectory model to accurately catch the landfall location of a TC because the TCs that make landfall often cause catastrophic disasters. In this study, a TC that makes landfall means that the TC crossed the land–sea boundary from ocean to land at least once. Note that only the first landing is taken into account. To count the number of TCs that make landfall in the present study, we have to rely also on the global relief model of Earth’s surface from National Geophysical Data Center that integrates land topography and ocean bathymetry at 0.1° resolution ( Amante and Eakins 2009 ).

It may be interesting to compare the numbers of historically recorded TCs that make landfall to the numbers obtained with the present TC trajectory model. A convincing comparison would certainly provide a valuable verification to the proposed model. For reference, results computed with Wu and Wang’s (2004) and Emanuel et al.’s (2006) models are also presented. In the western North Pacific basin, the total number of TC events observed during the period 1979–2018 is 935 as mentioned previously. Among these TCs, the observed landfall rate is 56% with 522 events. In comparison, the computed landfall rate is 59% with 555 events by the present model. It is 53% with 493 events by Wu and Wang’s (2004) model, and 38% with 358 events by Emanuel et al.’s (2006) model, respectively. The landfall rate computed with the present model is slightly higher than the observed landfall rate, but the total error is quite acceptable and is at the same level as the result given by Wu and Wang’s (2004) model.

Figure 5 shows the distribution of TC landfall number against latitudes. It is evident that the present TC trajectory model can reproduce the observed distribution much better than other models, with a correlation coefficient of 0.97. The maxima in the distribution are also well caught by the present model. In contrary, the number of TC landfalls is obviously underestimated at 22.5° and 35°N and overestimated at 10°–15°N with Wu and Wang’s (2004) model, and significantly underestimated at low latitudes with Emanuel et al.’s (2006) model. Note that an accurate representation of the maxima is not trivial because they are closely related to the number of TCs that make landfall at particular coasts (e.g., along the coast of the Philippines, the eastern coast of China, and the coasts of the Korean peninsula and Japan).

In this study, a simple TC trajectory model was proposed to properly consider the effect of the beta drift given the large-scale environmental flow condition. The horizontal shear in the environmental flow was assumed to be the key variable in a climatologically effective expression for the beta drift. To verify the performance of the proposed trajectory model, all historical TC tracks in the western North Pacific and the North Atlantic Ocean basins during the period 1979–2018 were simulated. The computed patterns of the TC occurrence frequency in the two ocean basins were demonstrated to be very close to those derived from observed data. The prevailing tracks averaged from the results obtained with the present trajectory model were also shown to have the best agreement with the relevant ones derived from observed data in the western North Pacific basin, as compared to the existing models that took different strategies to include the effect of the beta drift. The TC landfall rate as well as the latitudinal distribution of the TC landfall number computed with the proposed model also found the best agreement with those derived from observed data in the western North Pacific Ocean basin, as compared to existing models. It may then be possible to conclude that the proposed trajectory model is advantageous in representing the major climatological properties of TC tracks. It is thus promising to be integrated into a GCM for assessing the possible impact of climate change on TC activities. Note that, when integrated into a GCM, an effective method to initialize TC genesis is necessary.

Acknowledgments

This research is supported by National Natural Science Foundation of China (NSFC) under Grant 11732008.

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Scientists sound alarm over powerful geomagnetic storm engulfing Earth

T he powerful geomagnetic storm that recently hit the Earth has already caused radio and GPS to malfunction. Scientists emphasize that it can also cause a wave of hurricanes and storms.

RBC-Ukraine explains what theory the researchers have put forward, what it is based on, and whether the consequences of the magnetic storm threaten Ukrainians.

Sources: Daily Mail, Meteoagent, National Oceanic and Atmospheric Administration (NOAA).

Researchers' theory

According to a recent study, a solar storm that hits the Earth can cause hurricanes and storms.

As part of their work, a team of scientists used a model that:

・studied the activity of tropical cyclones over the past 5500 years

・identified 11 time periods when there were 40% more storms on Earth than usual

The only common feature of these periods was extreme solar activity.

Scientists' theory is that when the sun is overly active, it sends more energy to the Earth, which heats the oceans and becomes fuel for tropical storms.

Right now, the United States is in the midst of the so-called hurricane season, but given the solar activity, it could be a record-breaking one.

An active sunspot in May 2024 (screenshot: youtube.com/@noaanationalweatherservice)

Geomagnetic storms impact on Earth's orbit and surface

The National Oceanic and Atmospheric Administration (NOAA) also reported that geomagnetic storms can affect infrastructure in Earth orbit and on the Earth's surface.

According to experts, such solar activity can significantly affect communications, power grids, navigation, radio, and satellite operations.

Professor Yang Wang from Florida State University clarified that it is not always easy to predict the impact of the Sun on the Earth's surface. Therefore, it is not worthwhile to say definitively that the geomagnetic storm will increase the number of tropical cyclones this year.

She adds that the role of solar activity in modulating tropical cyclone activity is complex.

"As the oceans warm up, they have more energy available to be converted into tropical cyclone winds, thus creating more favorable conditions for the development of stronger storms," she explains.

The scientists note that solar activity can be an important driver of climate variability and tropical cyclone activity through its effects on atmospheric circulation, ocean currents, and sea surface temperatures.

The Sun's influence on the Earth (infographic: meteoagent.com)

Is there threat to Ukrainians?

Given that Ukraine is located relatively far from the ocean, it is unlikely to be threatened by tropical cyclones or powerful hurricanes.

At the same time, many studies conducted by scientists point to a possible relationship between magnetic storms and human activity.

The point is that super-powerful solar energy can negatively affect a person's mood and well-being.

Some people emphasize that health can deteriorate during magnetic storms, with headaches, heart problems, sleep disturbances, and weakness.

Earlier, we wrote that the planet was covered by a powerful red magnetic storm of the highest level.

Read also about magnetic storms in May - what days you should take care of your health.

Impacts of Tropical Cyclones on Employment—An Analysis Based on Meta-regression

• First Online: 24 April 2021

Cite this chapter

• Xianhua Wu 5 , 6 &
• Ji Guo 5 , 6  

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Tropical cyclones are one of the serious environmental disasters. However, researcher’s opinions are divided as to the impacts of tropical cyclones on labor employment. In order to investigate the general principle of tropical cyclones’ impact on employment, explore the reason of the divergence among existing research conclusions and put forward some suggestions for post-disaster reconstruction, this paper employed quantitative analysis of the literature—meta-regression analysis based on the existing literature. This paper studies the impact of tropical cyclones on the quantity of labor employed and employee remuneration from four aspects: industry dimension, time dimension, income dimension and tropical cyclone intensity, which clarifies the impact direction and intensity of the disaster in each dimension. The results show that: (1) Tropical cyclone disasters have greater impact on the employment of the primary industry, that is, the primary industry suffers the heaviest loss of employee remuneration. The impact of tropical cyclones on the employment quantity in the second and tertiary industries is greater, and the impact on the secondary industry is greater than the tertiary industry. (2) In the short term, the impact of tropical cyclones on employment is negative and the impact intensity is strong; in the medium and long term, the impact is positive and the intensity of impact is decreasing. Thus, through post-disaster restoration and reconstruction, the negative impact of tropical cyclones on the employment is gradually reduced. (3) Although Tropical cyclone disasters increased the quantity of labor employed from the low-income groups, it reduces their employment remuneration. In addition, the impact of disasters on the employment number of high-income groups is relatively small compared to that of low-income groups. (4) The higher the category of the tropical cyclone, the greater the positive impact on the employment of labor force. In the final part of the paper, the causes of these phenomena are analyzed, and suggestions are given on how to carry out post-disaster restoration and reconstruction activities. This paper is a useful supplement to the study of natural disasters’ impact on employment. The conclusions can provide reference for the emergency management of the disaster and the improvement of the labor market.

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The estimation results of the selected literature were all significant at the level of 10%, 5%, or 1%. Finally, the results were all included in meta regression analysis regardless of their significance to test their robustness.

The results of Meta-regression analysis were obtained by using software Stata11. Heteroskedasticity all eliminated by robust analysis; as for multicollinearity problem: since the significant correlation between industry variable and secondary industry variable is 0.592, and the significant correlation between industry variable and tertiary industry variable is 0.681, added that the secondary industry and tertiary industry variables are the core variables, the industry dummy variable is eliminated. In addition, short-term effects dummy variable, medium-term effects dummy variable and long-term effects dummy variable are also highly correlated. Therefore, the medium-term effects dummy variables are regarded as the base variables. The short-term effects dummy variables and the medium-term effects dummy variables are set as one group, and the medium-term effects dummy variables and the long-term effects dummy variables are set as another to carry out regression analysis. The analysis is also performed within the group consisting of the short-term effects dummy variables and the long-term effects dummy variables. The results show that in the short term, the impact of tropical cyclones on employment quantity is enormous, while the impact is marginal in the long run. This result is consistent with the conclusion of the study.

None of the literatures dealing with tropical cyclones’ impact on employment has data on the primary industry, so neither Table  5.3 nor Table  5.4 shows the dummy variables of the primary industry.

When using the binary selection model to analyze the positive and negative effects of employee remuneration, the authors find that the estimation coefficient of the primary industry is missing; this could be explained by two reasons. First, there is a serious collinearity problem between the primary industry and other explaining variables in the model. Correlation analysis shows that the primary industry is highly correlated with industry variable, which however has already been excluded from all the models. Second, the positive and negative regression coefficient between the primary industry and wage is very small, thus ignored by the Stata program automatically. In the first industry, the regression coefficient is very small, and it is ignored by the Stata program. It is classified as the second reason. It is the same with the panel data variables. Since the literature which involved with the impact of disasters on employee remuneration didn’t employ autoregressive moving average model, there is no ARMA variables in Tables  5.5 and 5.6 .

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Acknowledgements

Zhe Xu, Hui Liu, Ji Guo, Lei Zhou, Zheng Fu also made great contributions to this manuscript. We express our heartfelt thanks to them. This research was supported by: National Social and Scientific Fund Program of China (18ZDA052; 17BGL142; 16ZDA047); The Natural Science Foundation of China (91546117, 71373131).

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School of Economics and Management, Shanghai Maritime University, Shanghai, China

Prof. Xianhua Wu & Prof. Ji Guo

Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Wu, X., Guo, J. (2021). Impacts of Tropical Cyclones on Employment—An Analysis Based on Meta-regression. In: Economic Impacts and Emergency Management of Disasters in China. Springer, Singapore. https://doi.org/10.1007/978-981-16-1319-7_5

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DOI : https://doi.org/10.1007/978-981-16-1319-7_5

Published : 24 April 2021

Publisher Name : Springer, Singapore

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Online ISBN : 978-981-16-1319-7

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